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Be Properly Scared
Be properly scared and go on doing what you have to do.
-- Flannery O'Connor
Silly picture, dead serious quote. Flannery O'Connor wrote this line in a letter toward the very end of her short life. I admire her bravery, her darkness, her acceptance of the strange and grotesque in human nature. Her characters are disturbing, colorful, riveting. I just reread her short story, "The Heart of the Park," and still feel an odd combination of uneasy and thrilled. Read this partial paragraph from the second page of the story and see if you can resist the need to find the book to see what happens:
The park was the heart of the city. He had come to the city -- with a knowing in his blood -- he had established himself at the heart of it. Every day he looked at the heart of it; every day; and he was so stunned and awed and overwhelmed that just to think about it made him sweat. There was something, in the center of the park, that he had discovered. It was a mystery, although it was right there in a glass case for everybody to see and there was a typewritten card telling all about it right there. But there was something the card couldn't say and what it couldn't say was inside him, a terrible knowledge like a big nerve growing inside him.
Wow. I love that: "...a terrible knowledge like a big nerve growing inside him." I picture that mysterious knowledge spreading wildly, branching, racing to the periphery of his body, filling him with white hot electricity.
The main character in this story is Enoch Emery, a creepy and manipulative young man who has "wise blood like his daddy." What a name. O'Connor's characters all seem to have fascinating names like Sally Poker Sash, Hazel Motes, Buford Munson, Tom T. Shiftlet, and Lucynell.
What's in the glass case? Be "properly scared"....
"The Heart of the Park" was first published in Partisan Review in 1949. It later became part of Wise Blood. It also appears in Flannery O'Connor: The Complete Stories, which is where I reread it.
The photograph was taken in my yard on 8/27/08.
Labels: "The Heart of the Park", Flannery O'Connor, Wise Blood
I love Flannery O'Connor. She is another writer who set the stage for my own tastes and perspectives in literature.
Absolutely, same here. She created such strange little worlds, such memorably odd people and incidents. I never tire of her writing; I can revisit her work again and again. Like poetry.
Deborah Batterman said...
Of course I'm now completely intrigued at how 'The Heart of the Park" became part of "Wise Blood." I have her collected works, which don't include that particular story. The titles she gave to her works are pure poetry.
Hi, Deborah -- yes, her titles are so intriguing. Your comment makes me think of "Everything That Rises Must Converge!" "The Heart of the Park" is definitely worth reading.
Fall Falling
Pac-Man Puffball
Butterfly Tasting
Lost in Looking
Duende: Words Both Winged and Quilled

redpajama_set_name: RedPajamaCommonCrawl


Do you want to have a good impression of San Marino? On this page you can find holiday pictures of San Marino. These pictures are made by travellers. If you also have some pictures of San Marino, then you can put them up here. In the adjoining block is a link to add more pictures. You can find more pictures of San Marino on the region-, destination- and accommodation pages from Zoover.
Do you want to have a good impression of San Marino? On this page you can find holiday pictures of San Marino. These pictures are made by travellers. If you also have some pictures of San Marino, then you can put them up here. In the adjoining block is a link to add more pictures. You can find more pictures of San Marino on the region-, destination- and accommodation pages from Zoover. Find Zoover reviews of 1 Campsite, 13 Hotels and 2 Bed and Breakfasts in San Marino. The most reviewed destinations in San Marino are San Marino. You can find more destinations by clicking the tab 'destinations'.


"...she looks to have been found a good opportunity to open her account in this lowly contest."
A tricky race to decode considering that all five of these make their debuts, but Brandina makes plenty of appeal on paper, and she looks more than capable of making a winning start in this company. Money for any of the others would be significant, though on paper it is Alisa's Pearl who makes the most appeal.
Vomandla has been getting closer to that elusive win of late, hitting the frame the last twice, and she looks to have been found a good opportunity to open her account in this lowly contest. Rebel Wilson has been consistent without winning but is feared, and the same can be said for Zalika, who has returned a beaten favourite on several occasions but will win a race of this nature sooner or later.
Crome Yellow is still a maiden after 7 starts, but the form he has shown is solid for this level of race, and he sits a long way clear of the field. African Java is still open to improvement after two starts and she can take advantage if the selection fails to score.


Adventure travel refers to any type of unconventional travel that's a departure from the ordinary.
There are many types of adventure travel, and it's up to you what places you want to discover or what activities you want to try. We'll be sharing just a few of the many ways you can enjoy adventure travel, but our suggestions are just the tip of the iceberg.
If you have the budget for it, you may want to plan your own adventure travel trip from beginning to end. In other words, rather than signing up for a tour, you arrange everything and hire people who can show you the areas you'll be exploring. Your checkbook may feel a bigger hit than if you had made a conventional plan, but it will be well worth it. There are few places that you will not be able to find a guide to show you the sights keeping your budget in mind. You first have to identify where you want to go and what activities you're interested in. Just a few of the types of guides you may look for are those who offer fishing expeditions, hikes or a historical tour.
A bicycle tour is a way to get plenty of exercise and see the world in a more intimate way than from a car or tour bus. Keep in mind that riding a bike all day is quite demanding, so make sure you're in condition for the trip you choose. The possibilities for bike trips are varied indeed, whether you want to ride around Asia, Europe or Australia. There are also plenty of opportunities to travel by bike in North America, whether in the U.S or Canada. If you're an avid bike rider, there's no better way to see the countryside and experience the open air.
Have you ever seen the Iditarod sled dog race on television and thought it looked like a fun and exciting activity? If you want to try your hand at racing sled dogs, there are some places where you can have this opportunity.
If you don't want to travel all the way to Alaska, you can find sled dog tours in many other places in North America, such as Minnesota. This is still a fairly low key type of adventure travel, but it's being offered in more and more places today. There are quite a few companies out there these days that operate these kinds of adventures, as adventure travel is more prevalent than it ever has been. Despite whether you are partaking on an adventure travel trip with your whole family, your significant other or as a solo journey, there is a trip that is right for you. It is crucial to know precisely what you'll be doing on the trip and whether or not all of the attendees are prepared for it. We have discussed a few options for adventure travel and if you do your own research, you'll see there are endless possibilities.


Wow!!, i just got an email from someone in Oracle to let me know that the Privilege Analysis feature of Database Vault has had its licensing changed from this week to now be free as part of an Enterprise Edition database (EE). This feature used to be a cost option and required a Database Vault license. To use privilege analysis you didn't actually need to enabled Database Vault but you needed the license. This is no longer the case from this week; Also i have been told that the licensing is also retroactive to all 12c versions.
The license details for 18c are here.
I wrote an article back in 2015 and also did a webinar for SANS and Oracle on a practical walk-through of Database Vault and Oracle 12c and this included using and testing Privilege Analysis. You can read about that here.
Great news; nice to see security features no longer being cost options. This is good to have; privilege analysis and reduction of privileges to least rights is also key to a super-locked Oracle database.
I started this blog post a few weeks ago and kept adding to it from time to time but I have been incredibly busy helping people secure data in their Oracle databases that it has taken a long time to get it complete and ready to publish. The looming brexit here in the UK does not seem to have reduced the need to protect data held and used in Oracle databases and indeed the introduction of GDPR law in May has added extra teeth to the laws to protect peoples personal data. There seems to be an upsurge in the need for people to protect data held in Oracle databases.
I have been working on securing Oracle for almost 20 years and I was instrumental in some of the first lists on securing Oracle and also was the author of one of the first big papers ever written on securing Oracle. I have been teaching people to secure and protect data in Oracle databases and of course performing security audits of Oracle databases, locking down Oracle, helping people design audit trail solutions and of course all aspects of Oracle security design around things like audit, Database Vault, Label Security, VPD, TSDP, masking and obfuscation and all aspects of securing data in Oracle. I also have been selling our database security scanner for Oracle; PFCLScan as well as helping people protect PL/SQL with PFCLObfuscate and much more.
It is fair to say I live and breath Oracle security. So, after all these years of doing everything i can in the Oracle security world what is a "Super Locked Oracle Database"?.
I have seen many levels of security in Oracle databases over the many years that I have been helping secure them; some i have come to see as part of my work that are already secured to some level and some i have of course helped to become secure. I have never seen a database though that is truly truly locked down. I have to say that my definition of this statement may be (actually almost certainly it is) different to others views of the same statement. So for some people a well locked down and secured Oracle database may not be the same for me. Thats just my opinion and i can justify why in each case. One example, a simple example could be that someone thinks that their mediocre security is fine because in their view they have good network security and desktop security but my view is that you should not assume that the attacker does not get into your network or into your building and indeed the attacker may not be an attacker in the traditional sense of the word and it maybe that they work for you and are doing things outside of their realm. Then there is the case where the production Oracle database is pretty well secured BUT they copy the production data to a test or development system and because they reset the passwords that is fine... hmmm... not really; the data is still the data and now its easy to steal from the test system that no one is monitoring. I have also seen cases particularly on the west side of the Atlantic where the company has applied most of the CIS benchmark for Oracle or the Stig or the ISACA list; the problem here is that hardening does not protect your data generally.
This is a problem with Oracle and the traditional idea of hardening does not work. Imagine you apply all clauses of the CIS 12c document to a production Oracle database and then step back and pat yourself on the back that you are compliant with the CIS benchmark 12c. Well done; its a lot of work BUT is the data in the database any more secure than it was before you started from attack by a logged in user? well that depends of course on the design of your data model and the data security you have added. Oracle is a generic engine that allows you to specify the rules for the design of your data model (tables, views, synonyms.....) and also your code (PL/SQL, Java....) and you are supposed to also design security but my experience of in-house Oracle database applications and also third party pre-packaged applications is that security is usually an after thought and some are pretty bad; they put all of the data and code into one schema and then you connect the web server to the database as the same schema; why? well because its easy and the code works and you didn't need to think about security at all; it just functions; the most important job often is to get it functionally working and make sure its performant. I have hear the same stories many times; the new system goes live in one week and oh, by the way can we do security now? madness.
So, yes you may have applied all of the CIS benchmark and feel you will pass an external audit but how well secured is your actual data? Even if the whole of the CIS is applied it does nothing for the users, the grants on tables and views and procedures of the actual application. This is the difference between hardening and data security design. I have been teaching people and helping people in consulting work for many years to focus more of their effort and budget on the security of data. We are trying to secure data not trying to secure Oracle with no thought about the data thats processed in the database.
Back to the question; "What is a super locked Oracle database"?. For me this would be the ultimate secured database; focused on the task of ensuring that only the exact number of people can see the explicit data that they are supposed to see and only when they are supposed to see it. i.e. absolute least privilege and absolute data access controls.
In real life we never get to build truly secure Oracle databases like this because budgets, man power and timescales get in the way. Mostly because security of the data in the Oracle database is not considered until later in the project or maybe because you bought an application and it doesn't really do security of data very well.
A search on line for a very secure database or definition of one doesn't throw up such at all so i would have to conclude that i am not the only one who has never seen a truly truly locked down Oracle database out their in the wild. I am sure some people are now going to email me and tell me of their absolutely secured Oracle database and thats fantastic; i would love to hear but I am sure its not super locked.
This was an interesting project and in the end it went ahead in a much stripped down way from the initial discussions; for two reasons; It is hard to design and implement and second its hard to use the system after its in such a locked down state. Testing some things shows that absolute security like they imagined that they needed creates operational issues that must also be resolved. Also application development and deployment has issues; audit trails are difficult if we want to capture everything but not leak anything via the audit trail. There were many issues to cover and resolve and a truly truly locked database is hard to use. This is really like the idea that every single person in the organisation must have specific tasks and the data that they access and use has to be known fully and rules must be created that allow them to only use that data and no more and that data must only become visible at the right point in time exactly when they are supposed to see it.
If we wanted to create a super locked database we must start with the data and ignore Oracle and we must essentially make a matrix that shows for each user (real person) what exact data they are allowed to see and why and how and when and is it encrypted and is it masked and how is it audited. The n we must lay that over the Oracle database.
One of the most obvious problems with the Oracle database is its massive numbers of PUBLIC grants and the (tens of) thousands packages and procedures that are available either publicly direct or via other packages where the function is not directly accessed but is used via another public interface. No one knows what all this code does and whether its vulnerable to an exploit OR its use can abuse data in the database without an actual exploit such as SQL Injection. This is a major hurdle and is one that is a massive problem to solve.
We have two approaches (actually more approaches exist as well but...) ; the first is we de-install the default schemas that we don't use and which own some of this code; this works fine if we can de-install and if we don't need the code exposed by these users. This is a difficult problem as no one knows what these 30 to 50 default users do exactly in every case. (around 35 default accounts in a 10g, 11g, 12c, 18c legacy database and a cumulative total of more than 50 if we build a multi tenant database). Big issue. The second issue is that some of the functionally is installed into the SYS schema - i.e. packages owned by SYS so that the de-install of default schemas does not remove the risky code from the database.
Related to this issue is the PUBLIC grants. We could remove all public grants. There are a small number of posts online about people who have tried to do this and an unclear situation as to whether Oracle would support you and also what happens every time you install a patch or run catproc (i.e. a lot of the grants will be added back again). My view is that you can remove all the PUBLIC grants and it can be made to work; be aware that multienant makes this harder to do as a lot of it has to be done in the root container or the catcon.pl script has to be considered. This would only remove the PUBLIC grants and the invalid objects can be fixed by granting back to any remaining schema that needs the grant (if you already removed most default schemas then this is easier and also as part of that if you remove Java and XDB then again its easier). BUT this means any dynamic use of a PUBLIC grant is going to fail at run time and any patch will put the grants back so you need to script the removal.
If we truly want a super locked database then we must remove default schemas and PUBLIC grants BUT any inter-package exposure that is not PUBLIC would still be there and could potentially be used.
These can be supplemented by hardening, patching, context based security around access, users and data, obfuscation and masking and encryption, audit trails, removal of public grants, removal of all defaults, network controls and much more. We should also add secure coding and security of all PL/SQL; i.e. we can use context based security again either by hand with who_called_me or accessible lists. We can include firewalls and intrusion detection or prevention and its possible to build that yourself into the database for free (at least in terms of license costs).
This subject of how far can we go in securing Oracle and what are the effects of that security intrigues me; i.e. does it make working harder; I have agreed to do two talks at the Belgian tech days conference next year in February and one of the talks I will give is on this very subject. How far can we go in securing data and what are the effects.
One last point of note is that 18c XE has just been released and it includes most of the security cost options for free such as database vault; fantastic!!
If anyone needs help to really secure their data either in terms of training; consulting or in terms of automated vulnerability scanning for their Oracle database talk to me on info@petefinnigan.com and we can help you protect your data.


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Latest Football News, Soccer Results, Goals, Transfers talks and Rumors
Footkitchen provides comprehensive coverage of football and US soccer games, live scores, standing, transfer talks and rumors.
MLS_Major League Soccer
Hot Football Transfer Gossip: Mourinho 'plots £78m Pogba bid', Liverpool 'join Vardy race'
Boss of Goss Kevin Darling on how United's apparent persistence to land Pogba, City's view he is too expensive and a possible Anfieldian twist to Vardy-gate...
The impossible has happened: Manchester City have finally found a player who they consider too expensive.
But he's not too expensive for Manchester United, even though he'll cost 100 times more than they sold him for.
We're talking, of course, about Paul Pogba, who the Mirror says is being lined up for a £78m move to Old Trafford, four years after United received £800,000 in compensation from Juventus for him.
The price tag is irrelevant, because Jose Mourinho has reportedly demanded to have the Frenchman in his midfield - and what Mourinho wants, Mourinho gets. Or at least, Ed Woodward tries his hardest to get it for him. The club employed a similar tactic with previous manager Louis van Gaal, so there is no guarantee it will be remotely successful.
Another reason United can't be picky about the transfer fee is that it's obvious to anyone with a passing knowledge of footballers' brains that Pogba would rather join Barcelona or Real Madrid, because a) they're in the Champions League and b) they're better.
But they may not be prepared to pay £78m, as Man City evidently aren't. The Guardian reports that United's cross-city rivals have dropped their interest in Pogba, with Pep Guardiola of the opinion that there is better value elsewhere in the market.
The phrase "better value" is all relative, with the Mirror claiming that City are ready to offer Athletic Bilbao's injured defender Aymeric Laporte a £120,000-a-week salary, along with meeting his £40m buyout clause.
Liverpool may try to hijack Arsenal's £20m move for Jamie Vardy, claims the Star, which quotes a Leicester source (yes a Leicester source, not a Liverpool one) as saying the striker is a "perfect fit" for the Anfield club "because of his pace, work ethic and goal-scoring ability". Or in other words, the fact he is good at football. You don't need Hot Football Transfer Gossip to tell you that this whole story is completely silly.
Diego Costa wants to return to Atletico Madrid this summer, says the Metro, which would pave the way for Chelsea to launch a £31m move to Marseille's Michy Batshuayi, says the Mirror.
The Belgian striker has apparently snubbed West Ham because he wants to move to "a big club or a club in the Champions League". It's unclear whether he would include Chelsea in either of those categories.
The Blues have also apparently spoken to Roma's Radja Nainggolan, who wants a "big pay rise" to move to Stamford Bridge (Telegraph) as well as swooping for Juventus' 32-year-old right-back Stephan Lichtsteiner, who is set to be made surplus to requirements by Dani Alves' arrival in Turin (Sun).
Elsewhere, Liverpool and West Ham are interested in Barcelona's former Arsenal captain Thomas Vermaelen (Mirror), Man Utd have entered the race to sign Alvaro Morata (Marca) and Stoke striker Peter Crouch has declared that he wants to become a coach - although it's unclear whether he means in football or robot dancing (Star).
Liverpool 2-1 Man City: Philippe Coutinho the hero as Reds march on
Philippe Coutinho Philippe Coutinho netted a sumptuous winner for Liverpool as they beat Man City 2-1 to continue their surge up the ...
Arsenal Transfer News: £28.6m medical TOMORROW, £5m Aston Villa FIGHT, £25m Martinez BOOS
Geoffrey Kondogbia is reportedly set to join Inter Milan, subject to a medical According to Sky Sports Italia, Arsenal have missed out ...
Sky Sports expert says Philippe Coutinho will not be sold by Liverpool to Barcelona today
GUILLEM BALAGUE says Liverpool will not sell Philippe Coutinho today. Liverpool transfer news: Philippe Coutinho is wanted by Barcel...
Exclusive: Liverpool make second bid of £64.6m for Monaco's Thomas Lemar as they push to complete transfer
Liverpool have increased their offer for Monaco's Thomas Lemar to more than 70million euros (£64.6million) and are now pushing hard for a d...
Liverpool FC to sign FC Barcelona midfielder Alexander Hleb?
Liverpool and QPR are reportedly showing interest in signing Barcelona midfielder Alexander Hleb. The 30-year-old, who has returned to Cam...
Jamie Vardy and Riyad Mahrez: the perfect partners never destined for the top
Mahrez and Vardy L eicester City's head of recruitment, Steve Walsh, cast a steely gaze over the Stade Océane. After travelling to nort...
Arsenal transfer targets: Mbappe and Mahrez?
Regardless of whether Arsene Wenger stays or not, there is a real need for investment at the Emirates this summer if they are to win the Pr...
Man Utd news: Jose Mourinho SNUBBED players after FA Cup final defeat to Chelsea
JOSE MOURINHO didn't speak to his Manchester United players after their FA Cup final defeat to Chelsea, according to Nemanja Matic. ...
Chelsea 'ready to beat Liverpool to SHOCK Radamel Falcao'
Radamel Falcao could be lining up alongside Chelsea players next term (Picture: Getty) Chelsea are reportedly ready to beat Liverpool...
AC Milan reveal Liverpool striker Andy Carroll as shock transfer target
Andy Carroll Speculation about Luis Suarez moving away from Liverpool is still haunting the Reds, but now their worries have been doubl...
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BARCELONA,--(February 26, 2018)-ADTRAN®, Inc., (NASDAQ:ADTN), the leading provider of next-generation open networking solutions, today announced it will demonstrate its CORD-based fiber access solution at Mobile World Congress 2018 as part of the Coriant Multi-Sided Solutions Showcase. By partnering with Coriant, a best-in-breed transport and Data Center Interconnect (DCI) vendor, ADTRAN's demonstration will validate the strength of its ADTRAN Mosaic solution as the world's most open and complete SD-Access architecture by extending the architecture beyond the access to the transport network.
During the demonstration, ADTRAN will showcase its SDN-programmable 10Gbps NG-PON2 access solution that supports converged residential broadband, SLA-based enterprise and 5G front/backhaul services. The demo will feature SDN/NFV-enabled multi-layer, multi-domain provisioning between the Coriant Transcend™ Maestro Multidomain Controller and ADTRAN Mosaic SD-Access architecture as validation of open standard interworking across multi-vendor cloud platforms. Service providers will learn how they can build and operate best-of-breed networks inclusive of ADTRAN's CORD-based wavelength agile Nx10Gbps fiber access and Coriant's complete line of industry-leading aggregation to core transport solutions.
"SD-Access is an architectural shift from previous networks that have historically relied on closed, monolithic, vendor-specific management systems. Mosaic's open approach to networking eliminates that vendor lock-in, and at the same time enables operators to lower their costs for building, operating and maintaining their networks," said Chris Thompson, director of Mosaic software and solutions portfolio, ADTRAN. "We are eager to demonstrate these capabilities at Mobile World Congress with Coriant to showcase Mosaic's capabilities across the transport network, as well as with many other partners and applications soon."
Beyond ADTRAN and Coriant's demonstration at Mobile World Congress, Coriant is also the newest member of the Mosaic Open Network Alliance. Leveraging the open and programmable nature of ADTRAN's Mosaic solution, Coriant is integrating ADTRAN's SD-Access fiber access solution within the Coriant Transcend framework, providing an industry-leading open, agile and fully software-controlled end-to-end transport solution.
"Coriant is a strong advocate of open networking, which enables network operators to easily choose from a collection of best-in-class solutions from a variety of vendors to provide a truly differentiated network solution," said Robert Shore, senior vice president of global sales enablement, Coriant. "ADTRAN's SD-Access solution fills a critical gap in bringing open and programmable networking solutions all the way to the edge of the network. Coupled with Coriant's Multi-Sided Platform Partnership program, ADTRAN's Mosaic Open Network Alliance provides a community of uniquely vetted solutions that mitigate risk to network operators' businesses and accelerates solution integration."
The ADTRAN demonstration will take place at the Coriant Multi-Sided Solutions Showcase at Coriant's Booth at Hall 2, Stand 2I30. Mobile World Congress 2018 is held in Barcelona, Spain, February 26 - March 1, 2018.


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Alabama investors say they were swindled by Collin County company tied to Ken Paxton indictments
Dozens of Alabamans allege they were told a string of lies that convinced them and others to invest $20 million in the struggling startup.
Servergy offices in McKinney. The technology firm, for which Texas Attorney General Ken Paxton allegedly promoted stock, relocated to North Texas in 2011.(Staff)
By Lauren McGaughy
11:24 AM on Aug 10, 2016 CDT
AUSTIN — The company tied to Attorney General Ken Paxton's indictments lied to dozens of investors from Alabama about a "miracle" product it couldn't sell, stockholders allege in a lawsuit that seeks to recoup nearly $3 million from the firm.
Last year, Paxton was indicted on charges that he persuaded people to invest in the company, Servergy Inc., without telling them he was getting a commission on their stock purchases. At the same time, news broke that the federal government was investigating whether the company defrauded investors. Later, civil fraud charges were filed against Paxton and Servergy's chief executive, Bill Mapp III.
Now, dozens of Alabamans are suing, saying they invested $2.8 million in Servergy in 2013 after receiving "bogus" information about the firm's success from Mapp and other leaders. Their stock is now worthless, they said, and they want their money back.
Their lawsuit was filed in February but hasn't been previously reported. It was included as a "related pending case" in the federal lawsuit against Paxton. The attorney general is not mentioned by name and is not a target of the lawsuit, which was filed in Dallas County District Court, but the Alabama investors cite his indictment in their complaint.
Servergy has denied all allegations against it. A trial is set for April. Calls and emails to Dallas and Birmingham-based attorneys representing the Alabama investors were not returned. Paxton's attorney also did not respond to request for comment.
The suit is the latest blow to a struggling technology company thrust into the spotlight after the accusations against Paxton went public.
Startups often struggle to court investors and create viable products, but Servergy's problems have become a political football thanks to the company's ties to the embattled attorney general and Rep. Byron Cook, a Corsicana Republican who has lobbed the fraud accusations against Paxton.
Texas Attorney General Ken Paxton and his wife, Angela, entered the Merrill Hartman Courtroom in the 5th Court of Appeals at the George Allen Courts Building in Dallas on May 12. Paxton appeared in court to fight his three felony fraud indictments. (File Photo/Rose Baca)(Rose Baca / Staff Photographer)
In their suit, the Alabama investors said they were unaware the U.S. Securities and Exchange Commission was investigating Servergy until the federal government sued the company to force it to comply with a subpoena for documents. Servergy never informed them of the probe, they say.
They allege that over the course of nearly a year in 2013, Servergy's top officers held at least three "roadshow" presentations in their small city of Fairhope, hoping to entice new capital to help them fund a "miracle" server that was smaller yet more powerful than similar servers. Company representatives said they had pre-sold the server to Amazon, Netflix, NASA and the city of Beijing in China, and that Facebook, Walmart and CVS were also waiting on their product.
These claims "had no basis in fact," the Alabama investors allege.
"Servergy's claim that IBM and others were already waiting to buy out the Company, and that one such offer to purchase the Company already was in hand which would result in a quick 10-45% return for investors, was a lie," the lawsuit says. "Even the chart that Servergy provided investors to tout the Company's CTS-1000 server as compared to the competition was false."
With these promises in hand, however, Servergy was able to entice new investors in and around Fairhope to purchase $20 million in stock. William Staggers, a doctor living there, made the single largest investment of $400,000. He had never before bought stock through a private offering, the lawsuit said.
Two years earlier, in 2011, Paxton had helped this same company raise $840,000 from a handful of other investors in Texas and Florida.
Paxton never invested in the company. He told SEC investigators he tried to buy $100,000 in Servergy stock, but Mapp wouldn't take his money: "I can't take your money. God doesn't want me to take your money," Paxton said Mapp told him during a meeting at a McKinney Dairy Queen in 2011.
Paxton said he later accepted the shares as a gift, but he never claimed them either as income or a gift on financial documents he's required to file with the state.
By 2015, Servergy had spent the $26 million it raised in from both the Paxton and Fairhope stock offerings. It abandoned its "miracle" server and pivoted from hardware to software.
The lawsuit states an audit shows Servergy lost nearly $9.7 million by the end of 2014 and generated revenue of just more than $36,000 — a far cry from the $68 million it promised investors.
Lauren McGaughy. Lauren is an investigative reporter based in Austin where she focuses on government accountability, criminal justice and LGBTQ issues. Before joining the investigative team, she covered Texas politics for The News and Houston Chronicle, and Louisiana politics for The New Orleans Times-Picayune. She loves cats and comic books and cooks a mean steak.
lmcgaughy@dallasnews.com /laurenrmcgaughy @lmcgaughy https://www.instagram.com/laurenmcgaughy/ LinkedIn Iconhttps://www.linkedin.com/in/lauren-mcgaughy-05b49812/
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text:
\section{Introduction}
\label{intro}
Small and medium-sized enterprise (SME) is one of backbones in the national economy, whose development directly affects it.
However, due to the incomplete management system and the lack of appropriate financial indicators, the credit risk assessment process is usually time-consuming, and the evaluation result is often of low accuracy.
Therefore, in this paper, we are going to propose an appropriate method of credit risk assessment to target this problem.

Industry and academy always have a critical focus on how to measure enterprise credit risk.
Conventional approaches of assessment  mainly extract enterprise-related features, such as financial indicators, to predict enterprise solvency.
However, with the expansion of global market size in recent years, conventional approaches have lost their power of discrimination in the situations, where relations and interactions between SMEs are numerous and complicated.
An SME's financial status can be easily affected by some actions from its related other SMEs.
For example, the contagion risk is caused by associated credit entities, which besets many SMEs with the risk of default even in good financial conditions.
Therefore, rather than single financial indicators, relations and interactions between SMEs should be paid more attention in studying SME credit risk.

To model the relations and interactions, various entities and their relationships can be considered in the information networks \cite{gupta2017heteclass}.
In the previous, most of researchers studied the above problem with a homogeneous information network \cite{sun2013mining} consisting only one single relation type and one entity type.
However in SME setting, the structure of homogeneous information network may be a bit simple to explain the relationships between SMEs.
To not lose important information, a heterogeneous information network \cite{2016A} with complicated graph structure is more suitable to study the interaction between SMEs.
In the heterogeneous information network, meta paths (MP) \cite{2016A} are taken as a fundamental data structure to capture semantical relationships between entities.
Through MP, complicated relationships between entities can be systematically and concisely defined.
The path provides a clear view of how entities interact mutually in the information network.
In this paper, to assess the status of SMEs, we exploit the power of meta path to study how influences among financial entities spread in the information network of SMEs.

In our method,  we first build a heterogeneous information network of SMEs to describe interactive relationships between different entities associated with SME.
Figure \ref{fig:SMEs-HIN} is a toy example of Walmart heterogeneous information network, which demonstrates some possible connections of Walmart and its related entities.
For example, path ``$Walmart\xrightarrow{branch} Sam's\, CLUB\xrightarrow{report} The\,Daily$'' represents information that Walmart's branch enterprise, Sam's CLUB, is criticized by The Daily magazine; path ``$Walmart \xrightarrow{boardmember}{Jon\,Walton}\xrightarrow{founder}{Truenorth}$.'' represents information that Walmart's board member Jon Walton is also the founder of enterprise Truenorth; and path ``$Walmart\xrightarrow{invest}{Tiktok}\xrightarrow{produce}{Bytedance}$.'' represents information that the product Walmart has invested in was produced by enterprise Bytedance.
It is easy to see that through information networks, the interrelated relations between entities can be easily obtained.
By building information network of SMEs, we can not only obtain the self-related information but also the interactive information associated with the target enterprise.
\begin{figure}[pth]
      \begin{center}
             \includegraphics[width=3.5in]{graph.png}\newline
      \end{center}
             \caption{Walmart heterogeneous information network example. There exist multiple types of nodes in the network, such as \emph{enterprise} (Walmart, Sam's CLUB, Bytedance, Truenorth, Cocacola), \emph{person} (Sam Walton, Jon Walton, Doug McMillion), \emph{commodity} (Coke, Tiktok) and \emph{news} (DAILY, FOX).  Links between nodes represent relation connect entities, for example, Sam Walton is the \emph{founder} of Walmart and Sam's CLUB, Cocacola \emph{produce} coke, and the daily news \emph{report} a piece of news of  Walmart's \emph{branch} company Sam's CLUB etc.}
             \label{fig:SMEs-HIN}
\end{figure}

With the given information network of SMEs,  we propose a novel feature - meta path feature, to measure the impact through meta paths from one financial entity to another.
Unlike conventional financial indicators, the meta path feature can be defined and applied very flexibly.
The flexibility makes us able to evaluate the credit status of SMEs from various perspectives more comprehensively.
The proposed meta path feature can also explicitly show how much one entity can be affected by a specific logical path, which can provide an intuitive view for banks, lenders, and relevant experts to understand the credit risk faced by SMEs.
In this way, SME default can be effectively identified.

In the rest of this paper, section \ref{sec:related} introduces the SME credit risk evaluation method and the application of information networks.
Section \ref{sec:model} builds a model of SMEs' heterogeneous information network and proposes the meta path feature.
In section \ref{sec:impact}, by considering the ability of risk identification, four features are proposed based on meta path.
Section \ref{sec:experiments} presents the experiment on three real-world datasets and section \ref{sec:Conclusion} concludes the paper.

\section{Related work}
\label{sec:related}
The credit risk evaluation model of SMEs was first established by Edmister \cite{edmister1972empirical} in 1972, leading to the emergence of a large number of credit risk measurement index systems.
Most of the early credit evaluation models for SME at home and abroad follow the index system of credit evaluation model for large enterprises, that is, the extraction of some key financial indicators of enterprise financial statements.
Among these key financial indicators, profitability indicators \cite{cultrera2016bankruptcy}, such as operating profit ratio and ratio of profits to cost, and solvency indicators \cite{tian2015variable}, such as current and quick ratio, are used the most.
Besides, operational capacity indicators \cite{bauer2014hazard}, development capacity indicators \cite{sermpinis2018modelling}, and liquidity indicators \cite{sermpinis2018modelling} are added in many studies.
Since financial indicators alone can not lineate the complete picture of an enterprise, non-financial indicators such as, managers background \cite{2010Evaluation}, working experience \cite{2013Feature}, and enterprise internal structure \cite{2010Predicting} \cite{moro2013loan} are added for evaluation.
However, financial and non-financial indicators can not capture the contagion credit risk among financial entities since they are independent and do not consider the casual chain.

Recently, with the rapid improvement of computing capacity and the development of data mining technology, information network has gained much attention from researchers and makes excellent work in the field of clustering \cite{sun2009ranking} \cite{sun2012relation}, classification \cite{ji2010graph} \cite{wang2016text}, relation prediction \cite{sun2012will} \cite{popescul2003statistical} and recommendation \cite{shi2015semantic} \cite{ma2009learning}.
Researchers often use two kinds of information networks: the homogeneous information network and the heterogeneous information network.
The homogeneous information network builds with same type of objects and link relations.
For example, Jamali \cite{jamali2010matrix} builds a social network for user recommendation based on user ratings;
Ma \cite{ma2008sorec} builds a friend relationship prediction network based on personal relations.
These homogeneous information networks ignore the relationship between different objects and relations, which causes the loss of important information.
The concept of heterogeneous information network was first proposed by Sun \cite{2016A} in 2009.
It combines more information and contains logical semantics of different object types and link types.
For example, Wang \cite{wang2018shine} proposes a Signed Heterogeneous Information Network Embedding to capture the sentiment links of online social information by considering users with sentiment and social relations; Hosseini \cite{hosseini2018heteromed} used the heterogeneous information network with high dimensional data and rich relationships for medical diagnosis.
The heterogeneous information network is usually used to capture complicated semantic and logical relationships among different entities.

In the field of SME credit risk evaluation, a large amount of data related to enterprises has been accumulated, such as upstream and downstream enterprise information and relevant news information.
The heterogeneous relationships between different entities have also provided researchers with new ideas to find SME credit risk factors.
For example, Tsai \cite{tsai2017risk} pays attention to the impact of enterprise-related news information on the credit risk of SMEs;
Yin \cite{yin2020evaluating} utilizes legal judgments to evaluate the credit risk of SMEs.
Moro \cite{moro2013loan} takes the impact of SMEs and bank manager trust relationship on enterprise credit risk into consideration.
Tobback \cite{2017Bankruptcy} pays attention to the inter-enterprise relationship data for feature selection to measure the credit risk of SMEs;
Kou \cite{2020Bankruptcy} is focused on enterprise payment and transaction data information to measure the credit risk of SMEs.
However, all of their works are built on homogeneous information networks, most of which do not consider the heterogeneous information.
Therefore, in this paper, we build a heterogeneous information network for SMEs to more effectively evaluate SME credit risks, which considers both the heterogeneous information of SMEs and the semantic information carried by different SME entities.

\section{Model of SME Credit Risk}\label{sec:model}
To evaluate SME credit risk, conventional methods adopted by experts usually make their judgments only based on the features directly affecting SME default, such as asset-liability ratio, current ratio, and turnover rate, but not on logical relationships between SMEs, such as parent and subsidiary situations, upstream and downstream situations, enterprise director or high-level manager related situations.
For example, when a parent company defaults, the solvency of its subsidiaries will also be affected.
If the influences exerted by the parent company are neglected, its subsidiary company's default conditions will be overestimated.
Therefore, apart from the features directly affecting default, the logical relationships between SMEs should also be considered in evaluating SMEs' status.
Paying attention to different connections between SMEs can improve both the reliability and the interpretability of the evaluation.
This section will give a model of SME credit risk with logical relationships adopted.

\subsection{SME Heterogeneous Information Network}\label{subsec:def}

A heterogeneous information network \cite{2016A} is a classical data structure used to model objects and relations in a directed graph.
This graph structure has shown its superiority in representing and storing knowledge about the natural world for many applications \cite{2013Modeling} \cite{2013Recommendation} \cite{2019Cash}.
Given different objects in information networks, logical connections can be effectively constructed, and semantic relationships can be easily captured.
Hence, we also build our model in a information network which is defined as follows:
\begin{definition}\label{def:graph}
With a schema $S=(\mathcal{A},\mathcal{R})$, an \emph{information network} defined as a directed graph $G=(\mathcal{V},\mathcal{E})$ with object type function $\tau:\mathcal{V}\rightarrow\mathcal{A}$ and relation type function $\phi:\mathcal{E}\rightarrow\mathcal{R}$, where object $v\in\mathcal{V}$ belongs to object type $\tau(v)\in\mathcal{A}$, link $e\in\mathcal{E}$ belongs to relation type $\phi(e)\in\mathcal{R}$.
\end{definition}

In this paper, our model is built as a heterogeneous information network of SMEs.
The SME schema is shown in Figure \ref{fig:schema}.

\begin{figure}[pth]
      \begin{center}
            \includegraphics[width=3in]{schema.png}\newline
      \end{center}
             \caption{The SME network schema}
             \label{fig:schema}
\end{figure}

In our model, \emph{enterprise} ($\mathcal{A}_{e}$) , \emph{commodity} ($\mathcal{A}_{c}$), \emph{person} ($\mathcal{A}_{p}$) and \emph{news} ($\mathcal{A}_{n}$) are four fundamental object types in studying SME credit risk.
The studied relation types are summarized from public enterprise information and objective facts, such as the \emph{shareholder} relation between enterprise and person, the \emph{produce} relation between enterprise and commodity, and the \emph{report} relation between enterprise and news.
The types mentioned in this paper are listed in Table \ref{tab:den}.

\begin{table}
\label{tab:den}
\centering
\begin{tabular}{cll}
  \hline\noalign{\smallskip}
     Notation & Discriptions \\
     \noalign{\smallskip}\hline\noalign{\smallskip}
                    $\mathcal{A}_{e}$ & the object type of \emph{enterprise}\\
                    $\mathcal{A}_{c}$ & the object type of \emph{commodity}\\
                    $\mathcal{A}_{p}$ & the object type of \emph{person}\\
                    $\mathcal{A}_{n}$ & the object type of \emph{news}\\
                    $\mathcal{R}_{parent}$ & the relation type of \emph{parent} between enterprises\\
                    $\mathcal{R}_{subsidiary}$ & the relation type of \emph{subsidiary} between enterprises\\
                    $\mathcal{R}_{supplyer}$ & the relation type of \emph{supply} between enterprises\\
                    $\mathcal{R}_{saler}$ &  the relation type of \emph{sales} between enterprises\\
                    $\mathcal{R}_{control}$ & the relation type of \emph{controller} between enterprise and person\\
                    $\mathcal{R}_{shareholder}$ & the relation type of \emph{shareholder} between enterprise and person\\
                    $\mathcal{R}_{manager}$ &  the relation type of \emph{manager} between enterprise and person\\
                    $\mathcal{R}_{employee}$ &  the relation type of \emph{employee} between enterprise and person\\
                    $\mathcal{R}_{produce}$ & the relation type of \emph{produce} between enterprise and commodity\\
                    $\mathcal{R}_{report}$ & the relation type of \emph{report} between enterprise and news\\
                    $\mathcal{R}_{relate}$ & the relation type of \emph{relate} between person\\
  \noalign{\smallskip}\hline
\end{tabular}
\caption{Object type and relation type notations}
\end{table}

With the SME schema defined, an example of SME heterogeneous information network is shown in Figure \ref{fig:sme}.
\begin{figure}[pth]
      \begin{center}
             \includegraphics[width=3in]{sme.png}\newline
      \end{center}
             \caption{The SME heterogeneous information network}
             \label{fig:sme}
\end{figure}
We can see that $v_{1}$, $v_{2}$, $v_{7}$ are \emph{enterprise}, that we have $\tau(v_{1})=\mathcal{A}_{e}$, the same as $\tau(v_{2})$ and $ \tau(v_{7})$ are.
The $v_{6}$, $v_{9}$ are \emph{commodities}, that we have $\tau(v_{6})=\mathcal{A}_{c}$, the same as $\tau(v_{9})$.
The $v_{5}$, $v_{10}$, $v_{11}$, $v_{12}$, $v_{13}$ are \emph{news}, that we have $\tau(v_{5})=\mathcal{A}_{n}$, the same as $\tau(v_{10})$, $\tau(v_{11})$, $\tau(v_{12})$, and $\tau(v_{13})$ are.
The $v_{3}$, $v_{4}$, $v_{8}$ are \emph{persons}, that we have $\tau(v_{3})=\mathcal{A}_{p}$, the same as $\tau(v_{4})$ and $\tau(v_{8})$ are.
The $e_{5}$, $e_{8}$ are the relation of produces, that we have $\phi(e_{5})=\mathcal{R}_{produce}$, the same as $\phi(e_{8})$.
The $e_{4}$, $e_{9}$,$e_{10}$, $e_{11}$, $e_{12}$ are the relation of \emph{reports}, that we have $\phi(e_{4})=\mathcal{R}_{report}$, the same as $\phi(e_{9})$, $\phi(e_{10})$, $\phi(e_{11})$, and $\phi(e_{12})$ are.
The $e_{6}$ is the relation of \emph{supply}, $e_{1}$ is the relation of \emph{parent}, that we have $\phi(e_{6})=\mathcal{R}_{supply}$, and $\phi(e_{1})=\mathcal{R}_{parent}$.
The $e_{7}$, $e_{2}$ are the relation of \emph{controller}, $e_{3}$ is the relation of \emph{employee}, that we have $\phi(e_{7})=\mathcal{R}_{control}$, the same as $\phi(e_{2})$, and $\phi(e_{3})=\mathcal{R}_{employee}$.
The $e_{13}$ is the relation of \emph{relate}, that we have $\phi(e_{13})=\mathcal{R}_{relate}$.

\subsection{SME Meta Path}\label{subsec:meta}

In the SME network graph we build in Section \ref{subsec:def}, a graph edge is used to present the relationship between two objects.
Limited by the definition of edge, the represented relationships can only be some simple ones, which are insufficient to describe the relationships used in the problem of SME credit risk.
In order to model complicated relationships, in this section, we introduce another data structure, meta path (MP), to represent complicated and implicit relations in SME network.

\begin{definition}\label{def:meta}
With a schema $S=(\mathcal{A},\mathcal{R})$, a \emph{meta path} $P$ is a path in the form  $\mathcal{A}_{1} \stackrel{ \mathcal{R}_{1} }{\longrightarrow} \mathcal{A}_{2} \stackrel{ \mathcal{R}_{2} }{\longrightarrow}...\stackrel{ \mathcal{R}_{n} }{\longrightarrow} \mathcal{A}_{ n+1}$ which defines a composite relation $\mathcal{R} = \mathcal{R}_{1} \circ \mathcal{R}_{2} \circ \ldots \circ \mathcal{R}_{n}$ between $\mathcal{A}_{1}$ and $\mathcal{A}_{n+1}$, where $\circ$ denotes the composition operator on relations.
\end{definition}

For simplicity, we use the names of object types and relation types denoting the MP: $P = \mathcal{A}_{1}{\cdot}\mathcal{R}_{1}{\cdot} \mathcal{A}_{2} \ldots \mathcal{R}_{n}{\cdot}\mathcal{A}_{n+1}$.
With the definition of meta path, a path $p = v_{1}{\cdot}e_{1}{\cdot} v_{2} \ldots e_{n}{\cdot}v_{n+1} $ in graph $G$ follows a meta path $P$, if for any vertex $v_{i}\in\mathcal{V}$ and any edge $e_{i} \in \mathcal{E}$, there have edge $e_{i}$ is between $v_{i}$ and $v_{i+1}$, $\tau(v_{i})={\mathcal A_{i}}$  and $\phi(e_{i})={\mathcal R_{i}}$.
We also call $p$ as a \emph{path instance} of $P$ with the denotation $p\in P$.

According to the definition, some examples of meta paths can be seen in Figure \ref{fig:mpschema}.
\begin{figure}[pth]
      \begin{center}
             \includegraphics[width=4in]{mpschema.png}\newline
      \end{center}
             \caption{Example of meta paths}
             \label{fig:mpschema}
\end{figure}
$P = \mathcal{A}_{e}{\cdot}\mathcal{R}_{parent}{\cdot}\mathcal{A}_{e}{\cdot}\mathcal{R}_{report}{\cdot}\mathcal{A}_{n}$ is a MP, which represent the information that  the SME's parent enterprise  has report a news.
According to Figure \ref{fig:path}, there is a path instance $p = v_{1}{\cdot}e_{1}{\cdot} v_{2}{\cdot} e_{9}{\cdot}v_{10}$ of MP $P$.
Because $\tau(v_{1})=\mathcal{A}_{e}$, $\tau(v_{2})=\mathcal{A}_{e}$, $\tau(v_{10})=\mathcal{A}_{n}$, $\phi(e_{1})= \mathcal{R}_{parent}$, $\phi(e_{9})=\mathcal{R}_{report}$.
\begin{figure}[pth]
      \begin{center}
             \includegraphics[width=4in]{path.png}\newline
      \end{center}
             \caption{Example of path instances}
             \label{fig:path}
\end{figure}

The given MP definition structures logical connections between objects, making our model more expressive and interpretable.
It not only can show explicit reasons of factors affecting SMEs on credit risk, but also can explain implicit logics of correlation between objects having no direct links in SME information network.

Compared to the information carried by objects, the information carried by meta paths is more critical in evaluating the credit risk of SMEs.
The reason is that the expression ability of meta paths is more stronger.
Through different meta paths, the same financial object may affect another financial object significantly differently.
For instance, in Figure \ref{fig:path} we can see that there exist two paths from person $v_{4}$ to enterprise $v_{1}$.
The first one is $p = v_{1}{\cdot}e_{3}{\cdot} v_{4}$ following meta path  $P = \mathcal{A}_{e}{\cdot}\mathcal{R}_{employee}{\cdot} \mathcal{A}_{p}$ and the second one is $p = v_{1}{\cdot}e_{2}{\cdot} v_{3}{\cdot}e_{13}{\cdot} v_{4}$ following meta path $P = \mathcal{A}_{e}{\cdot}\mathcal{R}_{control}{\cdot} \mathcal{A}_{p}{\cdot}\mathcal{R}_{relate}{\cdot} \mathcal{A}_{p}$.
From the first path, the bribery scandal of an outsourcing employee $v_{4}$ may do limited harm to the enterprise $v_{1}$ since $v_{1}$ may have many other outsourcing employees to replace the role of $v_{4}$.
However, from the second path, the bribery scandal of the outsourcing employee $v_{4}$  may do significant harm to enterprise $v_{1}$ since $v_{4}$ has a domestic relation with $v_{3}$ who directs enterprise $v_{1}$.
Therefore, instead of inspecting each object's direct impact, our model regards a whole logical path consisting of objects and relations as a factor, in evaluating the credit risk of SMEs.

\section{Meta Path Impact On SME}\label{sec:impact}
In the above, we have given the definition of MP, a well-patterned structure to represent various semantics relating to SME credit risk.
It has been shown that even with no direct link given, the negative information of some SME may affect others heavily through meta paths.
For example, a piece of negative news about an enterprise director may lead to a bad reputation for his enterprise;
a low-quality product of a parent enterprise may cause a loss of competitiveness to its subsidiary enterprises.
Usually, potential risks brought from paths is non-trivial to be neglected when an SME is evaluated, but how to formulate such potential risk remains a question.
In order to solve this question, in this section, we will propose several novel features, named meta path feature, to represent the risk.

\subsection{Risk Inference from Object}\label{subsubsec:nega}
Before introducing meta path features, we first give a method to identify if there exists potential risk in financial objects themselves.
According to the object types studied in Section \ref{subsec:def}, except the\emph{news} object which is used to provide negative or positive information,
a \emph{commodity} object is regarded with potential risks if its quality is not reliable;
a \emph{person} object is regarded with potential risks if his capability is not qualified;
an \emph{enterprise} object is regarded with potential risks if it is lack of credibility.
In this paper, in order to inference if potential risks exist, considering applicability and generality, we use Naive Bayes model to inference if the mentioned objects is risky or not.
Our probabilistic model is learnt from public historical data, such as financial statements, annual reports, and online public news.
The definition of our Naive Bayes inference model is given as the following:
\begin{definition}\label{def:gamma}
    With the assumption that each attribute feature of an object is independent of each other, we define an inference function $\Gamma(x)$ to evaluate if object $x$ is risky based on the probability $\mathbb{P}(y=1|x)$ learned from Naive Bayes model.
    \begin{scriptsize}
        \begin{equation}
            \begin{aligned}
              \Gamma(x) &=
                    \begin{cases}
                          1               & \mathbb{P} ( y=1|x )>0.5\\
                          0               & \text{otherwise}
                         \end{cases}\\
              \mathbb{P} ( y=1|x ) &= \frac  { { \prod_{i} ^ {n} } \mathbb{P} (x ^ {(i)}|y=1) \mathbb{P} (y=1)}  { { \prod_{i} ^ {n} } \mathbb{P} (x ^ {(i)} |y=1) \mathbb{P} (y=1) + { \prod_{i} ^ {n} } \mathbb{P} (x ^ {(i)}|y=0) \mathbb{P}(y=0) }
            \end{aligned}
        \end{equation}
    \end{scriptsize}
\\where $x^{(i)}$ is the $i$th attribute feature of object $x$, $n$ is the number of all attributes, $y=1$ indicates risky object and $y=0$ indicates non-risky object.
\end{definition}

With the inference function, we are able to identify the risk of a financial object by its own information.
For instance, a \emph{commodity} object with low sales volume, high repair rate, and high refund will be inferred as risky one;
a \emph{person} object with irrelevant education background, irrelevant working experience, and short working years will be inferred as risky one;
an \emph{enterprise} object with low ROE ratio, low quick ratio, and high asset-liability ratio will be inferred as risky one.
In the next, we will study how to inference the potential risk from MP level.

\subsection{Risk Inference from Meta Path}\label{subsubsec:fea}
In an SME information network, an enterprise may have many paths linking to other financial objects, as shown in Figure \ref{fig:instance}.
We can see enterprise \emph{$J$} has $5$ path instances for meta path $P$ = $\mathcal{A}_{e}{\cdot}\mathcal{R}_{control}{\cdot}\mathcal{A}_{p}{\cdot}\mathcal{R}_{shareholder}{\cdot} \mathcal{A}_{e}$ and enterprise \emph{$K$} has $4$ path instances for MP $P$.

\begin{figure}[pth]
      \begin{center}
             \includegraphics[width=2in]{instance.png}\newline
      \end{center}
             \caption{The path instances of MP $P = \mathcal{A}_{e}{\cdot}\mathcal{R}_{control}{\cdot}\mathcal{A}_{p}{\cdot}\mathcal{R}_{shareholder}{\cdot}\mathcal{A}_{e}$. $J$ and $K$ are target SMEs, $L_{1}$,  $L_{2}$ and $L_{3}$ are controllers of $J$ and $K$.  $Q_{1}$,  $Q_{2}$, $Q_{3}$ and $Q_{4}$ are the associated enterprises of controllers $L_{1}$,  $L_{2}$ and $L_{3}$}.
             \label{fig:instance}
\end{figure}

With the inference function defined above, we are able to identify if objects in the above information network are risky or not.
Thus, for a specific MP, with the objects linked by its path instances, it is natural to infer that an enterprise is most likely to be risky if potential risks exist in most of its linked objects.
Based on this straight intuition, we next present several features to elaborate such risk from meta paths.

\subsubsection{Meta Path Feature}\label{subsubsec:fea}
Given an enterprise $x$, the number of risky objects connected by a MP $P$ are taken as an indicator to reflect the impact of meta path $P$ on target enterprise $x$.
The larger the indicator is, the higher the potential risk exists.
Formally, we call the indicator as naive MP feature and give its definition as the following.

\begin{definition}\label{def:ind}
    \emph{Naive MP feature $N_P(x)$} is an indicator to reveal the impact of meta path $P$ on enterprise $x$:
    \begin{scriptsize}
        \begin{equation}
            \begin{aligned}
              N_P(x)= \frac { | \{ x' \in D | \exists p_{x \rightsquigarrow x'} \in P,\Gamma(x')=1 \} |}  { | \{ x' \in D |  \exists p_{x \rightsquigarrow x'} \in P \} |} \\
             \end{aligned}
        \end{equation}
    \end{scriptsize}
\\where $D$ is an SME object collection, $p_{ x\rightsquigarrow x' }$ is a path instance from object $x$ to object $x'$ and  $\Gamma(x)$ is the inference function defined in Section \ref{subsubsec:nega}.
\end{definition}

In Figure \ref{fig:instance}, if $Q_{2}$, $Q_{3}$ and $Q_{4}$ are risky objects,
then we have $N_P(J)$ = $3/5$ = $0.6$, $N_P(K)$ = $3/4$ = $0.75$.

\subsubsection{Weighted Meta Path Feature}\label{subsubsec:weightedfea}
Although the above meta path feature can effectively indicate the impact of MP, it may be argued that the impact of different objects on the same MP should not be the same.
For all the objects in the network, irrelevant objects may affect small; relevant ones may matter big.
Especially for an SME, the enterprise, which is its parent company, should influence it deeper than the enterprise, which only has one cooperation with it.
Therefore, instead of treating all objects equally, it is more reasonable to treat them differently according to their relevance with the target SME.
Next, considering relevance between objects, we will give a relevance-weighted version of meta path feature accordingly.

Usually, relevance is used to measure how close two objects distance to each other.
As there is no unified definition of relevance, different applications have unique and appropriate relevance measures.
In SME application, there exists a usual fact that, even though an enterprise is of well financial status, it may also default, which is caused by the propagated negative influence of its related upstream and downstream enterprises.
Therefore, to measure the relevance between SME objects, a logical structure-based relevance measure is better than a textual context-based relevance measure.

A straightforward idea is that for any object pair, the two which have more paths should be more relevant.
From this idea, we simply introduce a path count version of MP weighted feature as follows.
\begin{definition}\label{def:npc}
    \emph{CountSim MP weight feature} $C_P(x)$ is an indicator to reveal the structure relevance impact of meta path P on enterprise $x$. We call it \emph{CountSim MP feature}.
    \begin{scriptsize}
        \begin{equation}
            \begin{aligned}
              C_P(x) = \frac{ | \{ x' \in D |  \exists p_{x \rightsquigarrow x'} \in P \} |}  {{{ | \{ x \in S\} } |}+{ | \{ x' \in S' \}} |} \\
             \end{aligned}
        \end{equation}
    \end{scriptsize}
\\where $S$ and $S'$ are SME object collections where all links from $x$ and to $x'$ respectively. $D$ is another SME object collection where contains all objects.
\end{definition}

The path count version is simple to apply but it make little use of graph structure.
In SME heterogeneous information network, logical relationships between objects are captured by the structure of graph paths.
Hence, compared to other measures, a path-based measure of relevance is more appropriate to be adopted in our model.
At last, we apply HeteSim \cite{shi2014hetesim}, an effective path-based similarity, to evaluate the relevance between objects.

\begin{definition}
    \emph{HeteSim MP weight feature $H_P(x)$ takes HeteSim as the similarity measure to reveal the path relevance impact of meta path P on enterprise $x$. We call it \emph{HeteSim MP feature}. }\\
    \begin{scriptsize}
        \begin{equation}
            \begin{aligned}
              H_P(x)=  \frac { { \sum\nolimits_{ x' \in  \{ x'| \exists p_{x \rightsquigarrow x'} \in P, \Gamma(x')=1 \} } } HeteSim(x,x') }  { { \sum\nolimits_{ x' \in  \{ x'| \exists p_{x \rightsquigarrow x'} \in P \} } } HeteSim(x,x') }
            \end{aligned}
        \end{equation}
    \end{scriptsize}
\\where $p_{ x\rightsquigarrow x' }$ is a path instance from object $x$ to object $x'$,  $HeteSim(x,x')$ is the relevance between object $x$ and object $x'$ under HeteSim and  $\Gamma(x)$ is the estimating function defined in Section \ref{subsubsec:nega}.
\end{definition}

\section{Experiments}\label{sec:experiments}
In this section, we are going to investigate the effectiveness of meta path features.
We conduct experiments on three real-world SME datasets.
The result and explanation is detailed in this part.

\subsection{Data and settings}\label{subsec:dataset}
In our experiments, three datasets recording enterprises' statistics are used for comparison.
GEM\footnote{The Growth Enterprise Market from Shenzhen Stock Exchange} and STAR\footnote{The Science and Technology Innovation Board from Shanghai Stock Exchange} datasets are about the SMEs of high technology, and SB\footnote{The Small and Medium-sized Enterprise Board from Shenzhen Stock Exchange} dataset are about traditional enterprises.
All the datasets can be downloaded from CSMAR (\url{https://www.gtarsc.com}).
As this paper only considers four types of financial entities (\emph{person}, \emph{commodity}, \emph{enterprise} and \emph{news}), our experiments are only performed on the enterprises those at least relate to one person, one commodity, one other enterprise and one piece of news.

\begin{table}
\centering
\begin{tabular}{lccc}
  \hline\noalign{\smallskip}
      & GEM & STAR & SB\\
     \noalign{\smallskip}\hline\noalign{\smallskip}
     number of enterprise & 528&297& 722\\
     related enterprise information & 58478&26554& 80729\\
      related person information & 360462 &38663& 515504\\
       related news information & 13026&3748&24718  \\
        related commodities information & 17450&8987 & 36735\\
  \noalign{\smallskip}\hline
\end{tabular}
\caption{Dataset information}
\label{tab:statistical}
\end{table}

The risk information about whether an enterprise is lack of credibilities, a person is lack of qualifications and a commodity is lack of reliabilities is obtained from CSMAR and cninf (\url{http://www.cninfo.com.cn}), which provide an authoritative and professional assessment on the entities.
The news information is collected from China Judgements Online \url{https://wenshu.court.gov.cn}.
The final details of datasets is shown in Table \ref{tab:statistical}.
As the gathered risk information may not be complete, for some important but unknown entities, we use the model in Section \ref{subsubsec:nega} to infer their risk.
If an entity's inferred probability is larger than $0.75$, it is deemed as risky.

Since the brought impact from a meta path decreases with its length increasing, we only consider the meta paths with length less than 6.
And, the meta paths which do not start with SME type are not selected for our experiments.
With the proposed MP features, we test their performance using a default prediction model which is used to learn the weights associated with those features.
The logistic regression model is taken as the prediction model, which is optimized by MLE (Maximum Likelihood Estimation).

In this section, all experiments were performed using Python 2.7.17 in Win $8.1+$ with CPU $i5-9300+$ processor and $8G+$ RAM.

\subsection{Selection of Meta Path Features}\label{subsec: mp feature selection}
Even limited by the length constraint, there may still exist numerous meta paths.
Among all possible meta path features, which ones are the most valuable ones?
In this section, we will run experiments to show the importance of meta path features.

We first generate 40 meta path features according to Definition \ref{def:ind} for simplicity.
Then each feature is tested under Wald test and the $p$-value of the feature associated with its meta path is used to evaluate the feature's importance.
The test is performed on all three datasets.
From the results, we list the top 20 significant meta path features for each dataset in Tables \ref{tab:1} - \ref{tab:SMEM} and the bottom 20 meta path features in Table \ref{tab:BGEMM} - \ref{tab:BSMEM} .
We can see that for all three datasets, the controller's ability, parent enterprise financial status, and news reported for enterprise, plays very significant roles in determining SME status.
However, the longer the relation chains, the worse the performance of MP features.
This may be due to the fact that longer links contain less valuable information.
Or the longer the chain, the more distracting and inaccurate information it contains.
Look into details, we find that for GEM and STAR datasets, the MP features containing personnel relations are most significant, while those containing enterprise relations are the least.
For SB dataset, the opposite is true.
It is reasonable, that the conventional SME, due to their own resource constraints, will pay more attention to the relationship with stakeholders in order to ensure stable development.
The high-technology SME mainly focus on technology research and development, so the ability of personnel has a significant impact on the enterprise.

\begin{table}
\centering
\begin{threeparttable}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 &  Meta path feature & P-value &Significance level\tnote{1}\\
\hline\noalign{\smallskip}
                   1&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$ & \textbf{3.7876e-46}&**** \\
                   2&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}$  &\textbf{5.3500e-37}&****\\
                   3&$\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&\textbf{1.7758e-32}&****\\
                   4&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e} $&1.0156e-32&****\\
                   5&$\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&3.9645e-29&****\\
                   6&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&8.3629e-26&****\\
                   7&$\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&2.2358e-26&****\\
                   8&$\mathcal{A}_{e}\cdot\mathcal{R}_{boardmember}\cdot\mathcal{A}_{p}$&6.1598e-23&****\\
                   9&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}$&2.4664e-15&****\\
                  10&$\mathcal{A}_{e} \cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&1.6067e-9&****\\
                  11&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$ & 3.7876e-6&**** \\
                  12&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e} $&5.3500e-5&****\\
                  13&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00121&***\\
                  14&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{relate}\cdot\mathcal{A}_{p}$&0.00160&***\\
                  15&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00236&***\\
                  16&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e} $&0.00246 &***\\
                  17&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.00396&***\\
                  18&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00615&***\\
                  19&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.00758&***\\
                   20&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}$& 0.00823&***\\
\noalign{\smallskip}\hline
\end{tabular}
\begin{tablenotes}
 \footnotesize
 \item[1] *: p\textless 0.1, **: p\textless 0.05, ***: p\textless0.01, ****: p\textless 0.001
\end{tablenotes}
\end{threeparttable}
\caption{Top -$20$ significant meta path features for the GEM dataset}
\label{tab:1}
\end{table}

\begin{table}
\centering
\begin{threeparttable}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 &  Meta path feature & P-value&Significance level\tnote{1} \\
\hline\noalign{\smallskip}
                    1&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$ & \textbf{7.4107e-44}&**** \\
                    2&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}$  &\textbf{3.3610e-37}&****\\
                    3&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}$&\textbf{1.8247e-29}&****\\
                    4&$\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n} $&1.8709e-22&****\\
                    5&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}$&1.925e-17&****\\
                    6&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&2.7723e-11&****\\
                    7&$\mathcal{A}_{e}\cdot\mathcal{R}_{boardmember}\cdot\mathcal{A}_{p}$&9.2910e-8&****\\
                    8&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n} $&2.8380e-4&****\\
                    9&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.000929&****\\
                   10&$\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&0.00175&***\\
                   11&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e}$ & 0.00277&*** \\
                   12&$\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$  &0.00283&***\\
                   13&$\mathcal{A}_{e}\cdot\mathcal{R}_{boardmember}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00341&***\\
                   14&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{p}$&0.0044&***\\
                   15&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.00455&***\\
                   16&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e} $&0.00476&***\\
                   17&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&0.00496&***\\
                   18&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00510&***\\
                   19&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00528&***\\
                   20&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00741&***\\
\noalign{\smallskip}\hline
\end{tabular}
\begin{tablenotes}
 \footnotesize
 \item[1] *: p\textless 0.1, **: p\textless 0.05, ***: p\textless0.01, ****: p\textless 0.001
\end{tablenotes}
\end{threeparttable}
\caption{Top-$20$ significant meta path features for the STAR dataset}
\label{tab:STARM}
\end{table}

\begin{table}
\centering
\begin{threeparttable}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 &  Meta path feature & P-value&Significance level\tnote{1} \\
\hline\noalign{\smallskip}
                    1&$\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$ & \textbf{1.2831e-48}&**** \\
                    2&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}$  &\textbf{3.0306e-45}&****\\
                    3&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p} $&\textbf{1.5510e-36}&****\\
                    4&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}$&6.5260e-35&****\\
                    5&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&3.7263e-35&****\\
                    6&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e}$&4.4973e-33&****\\
                    7&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e}$&2.3524e-33&****\\
                    8&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}$&1.1475e-28&****\\
                    9&$\mathcal{A}_{e}\cdot\mathcal{R}_{boardmember}\cdot\mathcal{A}_{p}$&6.8367e-27&****\\
                   10&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&5.2674e-13&****\\
                   11&$\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$ & 1.2831e-11&**** \\
                   12&$\mathcal{A}_{e}\cdot\mathcal{R}_{boardmember}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$  &3.0306e-9&****\\
                   13&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p} $&1.5510e-8&****\\
                   14&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{e}$&6.5260e-6&****\\
                   15&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}$&3.7263e-5&****\\
                   16&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{p}$&4.4973e-4&****\\
                   17&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e}$&2.3524e-4&****\\
                   18&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00114&***\\
                   19&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.00526&***\\
                   20&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$& 0.00683 &***\\
\noalign{\smallskip}\hline
\end{tabular}
\begin{tablenotes}
 \footnotesize
 \item[1] *: p\textless 0.1, **: p\textless 0.05, ***: p\textless0.01, ****: p\textless 0.001
\end{tablenotes}
\end{threeparttable}
\caption{Top-$20$ significant meta path features for the SB dataset}
\label{tab:SMEM}
\end{table}

\begin{table}
\centering
\begin{threeparttable}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 &  Meta path feature & P-value&Significance level\tnote{1} \\
\hline\noalign{\smallskip}
                   1&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}$ & 0.0783&* \\
                   2&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$  &0.0778&*\\
                   3&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{e}$&0.0788&*\\
                   4&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e} $&0.0832&*\\
                   5&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{e}$&0.0854&*\\
                   6&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n} $&0.0861&*\\
                   7&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.0874&*\\
                   8&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e}$&0.0889&*\\
                   9&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&0.0893&*\\
                  10&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e}$&0.0896&*\\
                  11&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$ & 0.0899&* \\
                  12&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$  &0.0932&*\\
                  13&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&0.1775&-\\
                  14&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p} $&2.4662&-\\
                  15&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{p}$&3.9645&-\\
                  16&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p} $&6.1598&-\\
                  17&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}$&7.4662&-\\
                  18&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&10.6710&-\\
                  19&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{e}$&12.4639&-\\
                   20&$\mathcal{A}_{e}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&16.0762&-\\
\noalign{\smallskip}\hline
\end{tabular}
\begin{tablenotes}
 \footnotesize
 \item[1] *: p\textless 0.1, **: p\textless 0.05, ***: p\textless0.01, ****: p\textless 0.001
\end{tablenotes}
\end{threeparttable}
\caption{Bottom-$20$ significant meta path features for the GEM dataset}
\label{tab:BGEMM}
\end{table}

\begin{table}
\centering
\begin{threeparttable}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 &  Meta path feature& P-value&Significance level\tnote{1} \\
\hline\noalign{\smallskip}
                    1&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{relate}\cdot\mathcal{A}_{p}$ & 0.0538&* \\
                    2&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e}$  &0.0598&*\\
                    3&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{e}$&0.0641&*\\
                    4&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n} $&0.0870&*\\
                    5&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.0873&*\\
                    6&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{e} $&0.0881&*\\
                    7&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.0886&*\\
                    8&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&0.0928&*\\
                    9&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&0.0941&*\\
                   10&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e}$&0.0951&*\\
                   11&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$ &0.0974&* \\
                   12&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$  &0.0976&*\\
                   13&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$&0.0982&*\\
                   14&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c} $&0.0987&*\\
                   15&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{e}$&4.6731&-\\
                   16&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p} $&7.7232&-\\
                   17&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&9.2910&-\\
                   18&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{e}$&12.8380&-\\
                   19&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&14.4176&-\\
                   20&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholeder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{e}$&17.5919&-\\
\noalign{\smallskip}\hline
\end{tabular}
\begin{tablenotes}
 \footnotesize
 \item[1] *: p\textless 0.1, **: p\textless 0.05, ***: p\textless0.01, ****: p\textless 0.001
\end{tablenotes}
\end{threeparttable}
\caption{Bottom-$20$ significant meta path features for the STAR dataset}
\label{tab:BSTARM}
\end{table}

\begin{table}
\centering
\begin{threeparttable}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
 &  Meta path feature & P-value&Significance level\tnote{1} \\
\hline\noalign{\smallskip}
                    1&$\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$ & 0.0714&* \\
                    2&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{report}\cdot\mathcal{A}_{n}$  &0.0730&*\\
                    3&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{relate}\cdot\mathcal{A}_{p} $&0.07551&*\\
                    4&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.07652&*\\
                    5&$\mathcal{A}_{e}\cdot\mathcal{R}_{parent}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}$&0.08352&*\\
                    6&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.08497&*\\
                    7&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&0.08632&*\\
                    8&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{produce}\cdot\mathcal{A}_{c}$&0.08756&*\\
                    9&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&0.09367&*\\
                   10&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{e}$&0.09526&*\\
                   11&$\mathcal{A}_{e}\cdot\mathcal{R}_{subsidiary}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$ & 0.09831&*\\
                   12&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$  &0.09836&*\\
                   13&$\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e} $&5.5101&-\\
                   14&$\mathcal{A}_{e}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{e}$&6.5260&-\\
                   15&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}$&9.7263&-\\
                   16&$\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{p}$&14.4973&-\\
                   17&$\mathcal{A}_{e}\cdot\mathcal{R}_{sales}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}$&23.5246&-\\
                   18&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{shareholder}\cdot\mathcal{A}_{p}$&27.7731&-\\
                   19&$\mathcal{A}_{e}\cdot\mathcal{R}_{control}\cdot\mathcal{A}_{p}\cdot\mathcal{R}_{employee}\cdot\mathcal{A}_{p}$&28.3672&-\\
                   20&$\mathcal{A}_{e}\cdot\mathcal{R}_{supply}\cdot\mathcal{A}_{e}\cdot\mathcal{R}_{manager}\cdot\mathcal{A}_{p}$&31.5267&-\\
\noalign{\smallskip}\hline
\end{tabular}
\begin{tablenotes}
 \footnotesize
 \item[1] *: p\textless 0.1, **: p\textless 0.05, ***: p\textless0.01, ****: p\textless 0.001
\end{tablenotes}
\end{threeparttable}
\caption{Bottom-$20$ significant meta path features for the SB dataset}
\label{tab:BSMEM}
\end{table}

\subsection{Overall Comparisons of MP feature}\label{subsubsec:Accuracy of evaluation measures}
In this section, we compare our three kinds of MP features with two kinds of other features proposed for evaluating SME credit risk.
One kind of the compared features is conventional features \cite{jrfm12010030},  such as current liquidity, quick ratio, assets turnover, a total of 16 financial indicators, and age of the enterprise, employment, a total of 5 non-financial indicators.
In our experiments, we call it \emph{SME CV}.
The other kind of the compared features is homogeneous path feature \cite{2017Bankruptcy} which is modeled from homogeneous information networks.
It contain only one object type and only one relation type, for example, two SMEs are related if they share a high-level manager.
In our experiments, we call it \emph{SME HPF}.
For our MP features, we respectively select the Naive MP features, CountSim MP features and HeteSim MP features according to the ranking result in Section \ref{subsec: mp feature selection} as the candidate features for comparison.
All the comparisons are still conducted on the mentioned three datasets.
To compare mentioned methods, we first select the top-10 performed features of each method.
And then we use their average AUC score as the overall score of each mentioned method.
The comparison results are summarized in Figures \ref{fig:auc1} - \ref{fig:auc3} and Table \ref{tab:auc}.

We can see that the heterogeneous MP features beats the SME CV features and the SME HPF features in all three datasets.
For the proposed MP features,  it turns out that: (1) All the MP features show better classification performance than the SME conventional features and the homogeneous path features; (2) The classification performance of the CountSim MP features and the HeteSim MP features beats the Naive MP features; (3)The classification performance of the CountSim MP features and the HeteSim MP features are similar.
The above results demonstrate the effectiveness of our proposed features in classifying default SMEs.

\begin{figure}[pth]
            \centering
            \includegraphics[width=3in]{auc1.png} \label{fig:auc1}
             \caption{ROC curve for the GEM dataset}
             \label{fig:auc1}
\end{figure}

\begin{figure}[pth]
            \centering
            \includegraphics[width=3in]{auc2.png}\label{fig:auc2}
             \caption{ROC curve for the STAR dataset}
             \label{fig:auc2}
\end{figure}

\begin{figure}[pth]
            \centering
            \includegraphics[width=3in]{auc3.png}
             \caption{ROC curve for the SB dataset}
             \label{fig:auc3}
\end{figure}

\begin{table}
\centering
\begin{tabular}{cccc}
  \hline\noalign{\smallskip}
       Features& GEM &STAR &SB\\
     \noalign{\smallskip}\hline\noalign{\smallskip}
       SME CV& 0.716  & 0.654 &0.721\\
       SME HPF& 0.728  & 0.707 & 0.733\\
       Naive MP& 0.747  & 0.759 & 0.752 \\
       CountSim MP& 0.771 & 0.767 & 0.756 \\
       HeteSim MP& 0.774  & 0.791 & 0.783 \\
  \noalign{\smallskip}\hline
\end{tabular}
\caption{average AUC score comparison for three datasets}
\label{tab:auc}
\end{table}

\subsection{Discussion}\label{discussion}
In this section, we will discuss some interesting point which we found in our experiments.
In general, prediction accuracy increases with data size increasing.
However, we found for SMEs the impact of data size is affected by the timestamp of data.
Next, we will detail and discuss how this affection comes.
Figure \ref{fig:timestamps} shows the average accuracy of meta path features under different timestamps.
\begin{figure}[pth]
            \centering
            \includegraphics[width=3in]{time1.png}
             \caption{average accuracy of MP features under different timestamps}
             \label{fig:timestamps}
\end{figure}

It is interesting that when we extend SME data used in our model with the latest data in one year, the accuracy of the model increases.
But if we extend that with data before last year, the accuracy of the model shows a declining trend.
This phenomenon may be due to the fact that if the additional data is still in its valid duration, our model can be learnt more fully within the life circle of the enterprise.
But if the additional data is out of its valid duration, our model may be learnt out of the life circle and loss its effectiveness.
For example,  employee turnover rate over two years can not reflect the truth about the target enterprise now.

\section{Conclusion}\label{sec:Conclusion}
This paper proposes a meta path based SME credit risk evaluation method that models SME-related information as a heterogeneous information network.
In detail, we first build an SME heterogeneous information network based on four entity types and ten relation types.
The heterogeneous information network of SMEs can capture the relationship among related enterprises and provide more comprehensive and reliable information for the credit risk measurement of SMEs.
Then, we extracted meta path features associated with SME based on the information network schema, which represents the situation of SME credit risk.
Finally, we developed three features to evaluate the effect of meta path on SME credit risks.
The experimental result shows that our proposed SME credit risk measuring method has a higher significance than the conventional features and the homogeneous features.

\begin{acknowledgements}
Thanks to the data provided by CSMAR.
Thanks to Python and the authors of different packages.
\end{acknowledgements}

 \section*{Conflict of interest}
 The authors declare that they have no conflict of interest.

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