To understand the vulnerability in having nodes in one network depend on nodes in another, consider the “smart grid,” an infrastructure system in which power stations are controlled by a telecommunications network that in turn requires power from the network of stations. In isolation, removing a few nodes from either network would do little harm, because signals could route around the outage and reach most of the remaining nodes. But in coupled networks, downed nodes in one automatically knock out dependent nodes in the other, which knock out other dependent nodes in the first, and so on. Scientists model this cascading process by calculating the size of the largest cluster of connected nodes in each network, where the answer depends on the size of the largest cluster in the other network. With the clusters interrelated in this way, a decrease in the size of one of them sets off a back-and-forth cascade of shrinking clusters.

We inspect a possible clustering structure of the corruption perception among 134 countries. Using the average linkage clustering, we uncover a well-defined hierarchy in the relationships among countries. Four main clusters are identified and they suggest that countries worldwide can be quite well separated according to their perception of corruption. Moreover, we find a strong connection between corruption levels and a stage of development inside the clusters. The ranking of countries according to their corruption perfectly copies the ranking according to the economic performance measured by the gross domestic product per capita of the member states. To the best of our knowledge, this study is the first one to present an application of hierarchical and clustering methods to the specific case of corruption.

Complex systems have multilevel dynamics emerging from interactions between their parts. Networks have provided deep insights into those dynamics, but only represent relations between two things while the generality is relations between many things. Hypergraphs and their related Galois connections have long been used to model such relations, but their set theoretic nature has inadequate and inappropriate structure. Simplicial complexes can better represent relations between many things but they too have limitations. Hypersimplices, which are defined as simplices in which the relational structure is explicit, overcome these limitations. Hypernetworks, which in the simplest cases are sets of hypersimplices, have a multidimensional connectivity structure which constrains those dynamics represented by patterns of numbers over the hypersimplices and their vertices. The dynamics of hypernetwork also involve the formation and disintegration of hypersimplices, which are seen as structural events related to system time. Hypernetworks provide algebraic structure able to represent multilevel systems and combine their top-down and bottom-up micro, meso and macro-dynamics. Hypernetworks naturally generalise graphs, hypergraphs and networks. These ideas will be presented in a graphical way through examples which also show the relevance of hypernetworks to policy. It will be argued that hypernetworks are necessary if not sufficient for a science of complex systems and its applications. The talk will be aimed at a general audience and no prior knowledge will be assumed.

Inspired by biological design and self-organizing systems, artist Heather Barnett co-creates with physarum polycephalum, a eukaryotic microorganism that lives in cool, moist areas. What can people learn from the semi-intelligent slime mold? Watch this talk to find out.

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.

Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.

Understanding the assembly of ecosystems to estimate the number of species at different spatial scales is a challenging problem. Until now, maximum entropy approaches have lacked the important feature of considering space in an explicit manner. We propose a spatially explicit maximum entropy model suitable to describe spatial patterns such as the species area relationship and the endemic area relationship. Starting from the minimal information extracted from presence/absence data, we compare the behavior of two models considering the occurrence or lack thereof of each species and information on spatial correlations. Our approach uses the information at shorter spatial scales to infer the spatial organization at larger ones. We also hypothesize a possible ecological interpretation of the effective interaction we use to characterize spatial clustering. (http://arxiv.org/abs/1407.2425)

A new theory can explain the formation of swarming patterns observed in ensembles of self-propelled polar particles.

Eugene Ch'ng's insight:

How do individual animals form swarms, schools, and flocks? In the 1990s, physicists modeled collections of self-propelled particles (so-called “active matter”) and could simulate the ordering that occurs in animal flocks. Theoretical models have reproduced many aspects of this collective behavior, but a number of questions have persisted. One concerns the observation that in polar, active matter—think of a collection of small, mutually interacting swimming arrows—the particles organize themselves into three possible pattern classes: density waves, solitary waves (solitons), and traveling “droplets.”

No single theory has been able to explain the formation and diversity of these patterns. However, in a paper in Physical Review Letters, Jean-Baptiste Caussin and collaborators from institutes in France, Germany, and the Netherlands, have solved a hydrodynamic model of polar active particles and have accounted for the origin and variety of these propagating swarm structures...

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided.

We introduce a novel description of the dynamics of the order book of financial markets as that of an effective colloidal Brownian particle embedded in fluid particles. The analysis of a comprehensive market data enables us to identify all motions of the fluid particles. Correlations between the motions of the Brownian particle and its surrounding fluid particles reflect specific layering interactions; in the inner-layer, the correlation is strong and with short memory while, in the outer-layer, it is weaker and with long memory. By interpreting and estimating the contribution from the outer-layer as a drag resistance, we demonstrate the validity of the fluctuation-dissipation relation (FDR) in this non-material Brownian motion process.

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities ($\bar{K} << 1$) and that the critical connectivity of stability $\bar{K}$ changes compared to random networks. At higher $\bar{K}$, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size $N$ increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

As information thunders through the digital economy, it’s easy to miss valuable “weak signals” often hidden amid the noise. Arising primarily from social media, they represent snippets—not streams—of information and can help companies to figure out what customers want and to spot looming industry and market disruptions before competitors do. Sometimes, companies notice them during data-analytics number-crunching exercises. Or employees who apply methods more akin to art than to science might spot them and then do some further number crunching to test anomalies they’re seeing or hypotheses the signals suggest. In any case, companies are just beginning to recognize and capture their value. Here are a few principles that companies can follow to grasp and harness the power of weak signals.

The same can be said for governing, although the end goal is, when it's actually working for the sake of the governing, how to better serve people according to their needs and expressed desires. The reward for good governance is continued time in office. The way you actually get to that end is through a combination of listening for NEEDS (which aren't the same as wants) within the general public and then actively teasing those needs out so that you can understand them.

It's a pro-active dialogue, especially on the part of the governing, if it is being done in a way that is actually beneficial for the governing and the governed alike. The former depends on the latter more than the latter depends on the former, because it is the governed which gives authority to the governing, while the governed can exist (if sub-optimally) without the governing group's present. It doesn't even matter which specific group is in power, since they're all going to be bound to do the same basic stuff in the same basic ways, if they're going to produce optimal results for themselves and other people living in the society as a whole. The only question that matters is "how well does the present governing group do at governing?" Society is constantly open to shopping for other options; constantly playing the field if things become sub-optimal for society in some way, shape or form.

That is why a good government is proactive when working with its citizens and listening for these "weak signals", because those are what reveals the subtle workings of the group's psychology and what the group actually is needing/wanting versus what they explicitly express.

The question What is Complexity? has occupied a great deal of time and paper over the last 20 or so years. There are a myriad different perspectives and definitions but still no consensus. In this paper I take a phenomenological approach, identifying several factors that discriminate well between systems that would be consensually agreed to be simple versus others that would be consensually agreed to be complex - biological systems and human languages. I argue that a crucial component is that of structural building block hierarchies that, in the case of complex systems, correspond also to a functional hierarchy. I argue that complexity is an emergent property of this structural/functional hierarchy, induced by a property - fitness in the case of biological systems and meaning in the case of languages - that links the elements of this hierarchy across multiple scales. Additionally, I argue that non-complex systems "are" while complex systems "do" so that the latter, in distinction to physical systems, must be described not only in a space of states but also in a space of update rules (strategies) which we do not know how to specify. Further, the existence of structural/functional building block hierarchies allows for the functional specialisation of structural modules as amply observed in nature. Finally, we argue that there is at least one measuring apparatus capable of measuring complexity as characterised in the paper - the human brain itself.

Virtual Heritage – the use of digital and virtual technologies for cataloguing and conveying our cultural heritage – offers exciting new ways to experience the cultural treasures of the world, both ancient and modern. Virtual heritage research in past decades has focused mainly on the visual aspect of heritage information processing. Optical scanning technology, remote sensing, sophisticated 3D modelling tools and developments in efficient computer graphics rendering pipelines have fuelled worldwide virtual reconstructions of tangible heritage. Such needs prompted funding councils and agencies to reserve and distribute resources in order to support the development of technologies and methodologies that made digital restoration, preservation and conservation possible. The visualisation and real-time interactive aspects of such developments have since provided access and availability of existing—and lost— tangible heritage to be studied and experienced via their virtual representations. As we review the present state of virtual heritage research, however, we realize that there remains a gap in the discipline. Most applications lacked life, behaviour, and intelligent agents in the virtual environments, and there has not been any progression in such developments since a decade ago. Reconstructions of heritage as elaborate virtual manifestations of materiality are without life, if they are without representations of life and behavior as intangible heritage representations in the virtual environment. The objective of this Special Issue on Living Virtual Heritage is to examine the state of development in the vibrant virtual heritage community.

Distributed intelligence is an ability to solve problems and process information that is not localized inside a single person or computer, but that emerges from the coordinated interactions between a large number of people and their technological extensions. The Internet and in particular the World-Wide Web form a nearly ideal substrate for the emergence of a distributed intelligence that spans the planet, integrating the knowledge, skills and intuitions of billions of people supported by billions of information-processing devices. This intelligence becomes increasingly powerful through a process of self-organization in which people and devices selectively reinforce useful links, while rejecting useless ones. This process can be modeled mathematically and computationally by representing individuals and devices as agents, connected by a weighted directed network along which "challenges" propagate. Challenges represent problems, opportunities or questions that must be processed by the agents to extract benefits and avoid penalties. Link weights are increased whenever agents extract benefit from the challenges propagated along it. My research group is developing such a large-scale simulation environment in order to better understand how the web may boost our collective intelligence. The anticipated outcome of that process is a "global brain", i.e. a nervous system for the planet that would be able to tackle both global and personal problems.

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities.

In one important way, the recipient of a heart transplant ignores its new organ: Its nervous system usually doesn’t rewire to communicate with it. The 40,000 neurons controlling a heart operate so perfectly, and are so self-contained, that a heart can be cut out of one body, placed into another, and continue to function perfectly, even in the absence of external control, for a decade or more. This seems necessary: The parts of our nervous system managing our most essential functions behave like a Swiss watch, precisely timed and impervious to perturbations. Chaotic behavior has been throttled out.

Or has it? Two simple pendulums that swing with perfect regularity can, when yoked together, move in a chaotic trajectory. Given that the billions of neurons in our brain are each like a pendulum, oscillating back and forth between resting and firing, and connected to 10,000 other neurons, isn’t chaos in our nervous system unavoidable?

In this paper we study the spreading speed of complex contagions in a social network. A k-complex contagion starts from a set of initially infected seeds such that any node with at least k infected neighbors gets infected. Simple contagions, i.e., k=1, spreads to the entire network quickly in any small world graph. However, the spreading of complex contagions appears to be less likely and more delicate; the successful cases depend crucially on the network structure~\cite{Ghasemiesfeh:2013:CCW}. The main result in this paper is to show that complex contagions can spread fast in the preferential attachment model, covering the entire network of nnodes in O(logn) steps, if the initial seeds are the oldest nodes in the network. We show that the choice of the initial seeds is crucial. If the initial seeds are uniformly randomly chosen and even if we have polynomial number of them, it is not enough to spread a complex contagion. The oldest nodes in a preferential attachment model are likely to have high degrees in the network. However, we remark that it is actually not the power law degree distribution per se that supports complex contagion, but rather the evolutionary graph structure of such models. The proof generalizes to a bigger family of time evolving graphs where some of the members do not have a power-law distribution. The core of the proof relies on the analysis of a multitype branching process, which may be of independent interest. We also present lower bounds for the cases of Kleinberg's small world model that were not analyzed in prior work. When the clustering coefficient γ is anything other than 2, a complex contagion necessarily takes polynomial number of rounds to spread to the entire network.

“Now, working elbow to elbow with billionaires, I was a giant fireball of greed.” –Sam Polk BREAK it down: Obscenely rich people from 2013 meet in Davos, Switzerland, to discuss how to get ludicrously rich in 2014; Japan’s Abe reminds Western...

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

Real-time social media like Twitter could be used to track HIV incidence and drug-related behaviors with the aim of detecting and potentially preventing outbreaks. The study suggests it may be possible to predict sexual risk and drug use behaviors by monitoring tweets, mapping where those messages come from and linking them with data on the geographical distribution of HIV cases. The use of various drugs had been associated in previous studies with HIV sexual risk behaviors and transmission of infectious disease.

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are not effective and sufficient to contain them. The failure of many conventional approaches results from their neglection of feedback loops, instabilities and/or cascade effects, due to which equilibrium models do often not provide a good picture of the actual system behavior. However, the complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be understood by means of complexity science, which enables one to address the aforementioned problems more successfully. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

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