Adversarial Attacks on Graph Neural Networks via Meta Learning
Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.
 This paper proposes an algorithm to alter the structure of a graph by adding/deleting edges so as to degrade the global performance of node classification. The main idea is to use the idea of meta-gradients from meta-learning to solve the bilevel optimization problem. The paper is clearly presented. The main contribution is to use meta-learning to solve the bilevel optimization in the discrete graph data using greedy selection approach. From the experimental results, this treatment is really effective in attacking the graph learning models (GCN, CLN, DeepWalk). However, the motivation in using meta-learning to solve the bilevel optimization is not very clear to me, e.g., what are the advantages it can offer? Theoretically, the paper could have given some discussion on the optimality of the meta-gradient approach to bilevel optimization to strengthen the theoretical aspect. For the greedy selection approach in Eq (8), is there any sub-modularity for the score function used? Some minor suggestions and comments: 1) please summarize the attacking procedures in the form of an algorithm 2) please have some discussion on attacking the graph attributes besides the structure 3) please have an complexity analysis and empirical evaluations of the meta-gradient computations and approximations
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Adversarial Attacks on Graph Neural Networks via Meta Learning
Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.
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>



 This paper proposes an algorithm to alter the structure of a graph by adding/deleting edges so as to degrade the global performance of node classification. The main idea is to use the idea of meta-gradients from meta-learning to solve the bilevel optimization problem. The paper is clearly presented. The main contribution is to use meta-learning to solve the bilevel optimization in the discrete graph data using greedy selection approach. From the experimental results, this treatment is really effective in attacking the graph learning models (GCN, CLN, DeepWalk). However, the motivation in using meta-learning to solve the bilevel optimization is not very clear to me, e.g., what are the advantages it can offer? Theoretically, the paper could have given some discussion on the optimality of the meta-gradient approach to bilevel optimization to strengthen the theoretical aspect. For the greedy selection approach in Eq (8), is there any sub-modularity for the score function used? Some minor suggestions and comments: 1) please summarize the attacking procedures in the form of an algorithm 2) please have some discussion on attacking the graph attributes besides the structure 3) please have an complexity analysis and empirical evaluations of the meta-gradient computations and approximations
assistant
 This paper proposes an algorithm to alter the structure of a graph by adding/deleting edges so as to degrade the global performance of node classification. The main idea is to use the idea of meta-gradients from meta-learning to solve the bilevel optimization problem. The paper is clearly presented. The main contribution is to use meta-learning to solve the bilevel optimization in the discrete graph data using greedy selection approach. From the experimental results, this treatment is really effective in attacking the graph learning models (GCN, CLN, DeepWalk). However, the motivation in using meta-learning to solve the bilevel optimization is not very clear to me, e.g., what are the advantages it can offer? Theoretically, the paper could have given some discussion on the optimality of the meta-gradient approach to bilevel optimization to strengthen the theoretical aspect. For the greedy selection approach in Eq (8), is there any sub-modularity for the score function used? Some minor suggestions and comments: 1) please summarize the attacking procedures in the form of an algorithm 2) please have some discussion on attacking the graph attributes besides the structure 3) please have an complexity analysis and empirical evaluations of the meta-gradient computations and approximations