Characterizing Malicious Edges targeting on Graph Neural Networks
Deep neural networks on graph structured data have shown increasing success in various applications. However, due to recent studies about vulnerabilities of machine learning models, researchers are encouraged to explore the robustness of graph neural networks (GNNs). So far there are two work targeting to attack GNNs by adding/deleting edges to fool graph based classification tasks. Such attacks are challenging to be detected since the manipulation is very subtle compared with traditional graph attacks. In this paper we propose the first detection mechanism against these two proposed attacks. Given a perturbed graph, we propose a novel graph generation method together with link prediction as preprocessing to detect potential malicious edges. We also propose novel features which can be leveraged to perform outlier detection when the number of added malicious edges are large. Different detection components are proposed and tested, and we also evaluate the performance of final detection pipeline. Extensive experiments are conducted to show that the proposed detection mechanism can achieve AUC above 90% against the two attack strategies on both Cora and Citeseer datasets. We also provide in-depth analysis of different attack strategies and corresponding suitable detection methods. Our results shed light on several principles for detecting different types of attacks.
 The study of detecting malicious edges in graphs is interesting and important. However, the significance of the paper can be improved. To properly test the detection performance, I recommend that the authors run experiments on various random graph models. Examples of random graph models include Erdos-Renyi, Stochastic Kronecker Graph, Configuration Model with power-law degree distribution, Barabasi-Albert, Watts-Strogatz, Hyperbolic Graphs, Block Two-level Erdos-Renyi, Multiplicative Attribute Graph Model, etc. That way we can learn on what types of networks the detection performance is better. Also, in terms of detection models, I recommend that the authors try approaches that look for goodness of fit and model selection (e.g., see https://arxiv.org/pdf/1806.11220.pdf).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Characterizing Malicious Edges targeting on Graph Neural Networks
Deep neural networks on graph structured data have shown increasing success in various applications. However, due to recent studies about vulnerabilities of machine learning models, researchers are encouraged to explore the robustness of graph neural networks (GNNs). So far there are two work targeting to attack GNNs by adding/deleting edges to fool graph based classification tasks. Such attacks are challenging to be detected since the manipulation is very subtle compared with traditional graph attacks. In this paper we propose the first detection mechanism against these two proposed attacks. Given a perturbed graph, we propose a novel graph generation method together with link prediction as preprocessing to detect potential malicious edges. We also propose novel features which can be leveraged to perform outlier detection when the number of added malicious edges are large. Different detection components are proposed and tested, and we also evaluate the performance of final detection pipeline. Extensive experiments are conducted to show that the proposed detection mechanism can achieve AUC above 90% against the two attack strategies on both Cora and Citeseer datasets. We also provide in-depth analysis of different attack strategies and corresponding suitable detection methods. Our results shed light on several principles for detecting different types of attacks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The study of detecting malicious edges in graphs is interesting and important. However, the significance of the paper can be improved. To properly test the detection performance, I recommend that the authors run experiments on various random graph models. Examples of random graph models include Erdos-Renyi, Stochastic Kronecker Graph, Configuration Model with power-law degree distribution, Barabasi-Albert, Watts-Strogatz, Hyperbolic Graphs, Block Two-level Erdos-Renyi, Multiplicative Attribute Graph Model, etc. That way we can learn on what types of networks the detection performance is better. Also, in terms of detection models, I recommend that the authors try approaches that look for goodness of fit and model selection (e.g., see https://arxiv.org/pdf/1806.11220.pdf).
assistant
 The study of detecting malicious edges in graphs is interesting and important. However, the significance of the paper can be improved. To properly test the detection performance, I recommend that the authors run experiments on various random graph models. Examples of random graph models include Erdos-Renyi, Stochastic Kronecker Graph, Configuration Model with power-law degree distribution, Barabasi-Albert, Watts-Strogatz, Hyperbolic Graphs, Block Two-level Erdos-Renyi, Multiplicative Attribute Graph Model, etc. That way we can learn on what types of networks the detection performance is better. Also, in terms of detection models, I recommend that the authors try approaches that look for goodness of fit and model selection (e.g., see https://arxiv.org/pdf/1806.11220.pdf).