Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction

• URL: http://arxiv.org/abs/2504.06492v1
• Date: Tue, 08 Apr 2025 23:36:29 GMT
• Title: Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
• Authors: Mingchen Li, Di Zhuang, Keyu Chen, Dumindu Samaraweera, Morris Chang,
• Abstract summary: Link prediction in graph data utilizes various algorithms and machine learning/deep learning models to predict potential relationships between graph nodes.<n>Recent research has highlighted the vulnerability of link prediction models to adversarial attacks, such as poisoning and evasion attacks.<n>This article proposes an unweighted graph poisoning attack approach using meta-learning techniques to undermine VGAE's link prediction performance.
• Score: 12.179477926103353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Link prediction in graph data utilizes various algorithms and machine learning/deep learning models to predict potential relationships between graph nodes. This technique has found widespread use in numerous real-world applications, including recommendation systems, community networks, and biological structures. However, recent research has highlighted the vulnerability of link prediction models to adversarial attacks, such as poisoning and evasion attacks. Addressing the vulnerability of these models is crucial to ensure stable and robust performance in link prediction applications. While many works have focused on enhancing the robustness of the Graph Convolution Network (GCN) model, the Variational Graph Auto-Encoder (VGAE), a sophisticated model for link prediction, has not been thoroughly investigated in the context of graph adversarial attacks. To bridge this gap, this article proposes an unweighted graph poisoning attack approach using meta-learning techniques to undermine VGAE's link prediction performance. We conducted comprehensive experiments on diverse datasets to evaluate the proposed method and its parameters, comparing it with existing approaches in similar settings. Our results demonstrate that our approach significantly diminishes link prediction performance and outperforms other state-of-the-art methods.

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