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Graph Neural Networks (GNN) offer the powerful approach to node classification in complex networks across many domains including social media, E-commerce, and FinTech. However, recent studies show that GNNs are vulnerable to attacks aimed at adversely impacting their node classification performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the connectivity between existing nodes, a task that requires greater effort on the part of the attacker in real-world applications. In contrast, it is much more expedient on the part of the attacker to inject adversarial nodes, e.g., fake profiles with forged links, into existing graphs so as to reduce the performance of the GNN in classifying existing nodes. Hence, we consider a novel form of node injection poisoning attacks on graph data. We model the key steps of a node injection attack, e.g., establishing links between the injected adversarial nodes and other nodes, choosing the label of an injected node, etc. by a Markov Decision Process. We propose a novel reinforcement learning method for Node Injection Poisoning Attacks (NIPA), to sequentially modify the labels and links of the injected nodes, without changing the connectivity between existing nodes. Specifically, we introduce a hierarchical Q-learning network to manipulate the labels of the adversarial nodes and their links with other nodes in the graph, and design an appropriate reward function to guide the reinforcement learning agent to reduce the node classification performance of GNN. The results of the experiments show that NIPA is consistently more effective than the baseline node injection attack methods for poisoning graph data on three benchmark datasets.
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A Bayesian Approach to High-Order Link Prediction
Using a subset of observed network links, high-order link prediction (HOLP) infers missing hyperedges, that is links connecting three or more nodes. HOLP emerges in several applications, but existing approaches have not dealt with the associated predictor’s performance. To overcome this limitation, the present contribution develops a Bayesian approach and the relevant predictive distributions that quantify model uncertainty. Gaussian processes model the dependence of each node to the remaining nodes. These nonparametric models yield predictive distributions, which are fused across nodes by means of a pseudo-likelihood based criterion. Performance is quantified by proper measures of dispersion, which are associated with the predictive distributions. Tests on benchmark datasets demonstrate the benefits of the novel approach.
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- PAR ID:
- 10518934
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2379-190X
- ISBN:
- 979-8-3503-4485-1
- Page Range / eLocation ID:
- 13251 to 13255
- Format(s):
- Medium: X
- Location:
- Seoul, Korea, Republic of
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP48485.2024.10448101
