Abstract
Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data. However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures. Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline. However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness. To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods. Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information. Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges. Additionally, we also provide an improved GNN as classifier. Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE. Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks.
Original language | English |
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Title of host publication | Proceedings of the 39th AAAI Conference on Artificial Intelligence, AAAI 2025 |
Editors | Toby Walsh, Julie Shah, Zico Kolter |
Publisher | AAAI press |
Pages | 13473-13482 |
Number of pages | 10 |
ISBN (Electronic) | 157735897X, 9781577358978 |
DOIs | |
Publication status | Published - 11 Apr 2025 |
Event | 39th AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States Duration: 25 Feb 2025 → 4 Mar 2025 https://ojs.aaai.org/index.php/AAAI/issue/archive (Conference Proceedings) |
Publication series
Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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Number | 12 |
Volume | 39 |
ISSN (Print) | 2159-5399 |
ISSN (Electronic) | 2374-3468 |
Conference
Conference | 39th AAAI Conference on Artificial Intelligence, AAAI 2025 |
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Country/Territory | United States |
City | Philadelphia |
Period | 25/02/25 → 4/03/25 |
Internet address |
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