Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement

Shuman Zhuang, Zhihao Wu*, Zhaoliang Chen, Hong Ning Dai, Ximeng Liu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

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 languageEnglish
Title of host publicationProceedings of the 39th AAAI Conference on Artificial Intelligence, AAAI 2025
EditorsToby Walsh, Julie Shah, Zico Kolter
PublisherAAAI press
Pages13473-13482
Number of pages10
ISBN (Electronic)157735897X, 9781577358978
DOIs
Publication statusPublished - 11 Apr 2025
Event39th AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025
https://ojs.aaai.org/index.php/AAAI/issue/archive (Conference Proceedings)

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number12
Volume39
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference39th AAAI Conference on Artificial Intelligence, AAAI 2025
Country/TerritoryUnited States
CityPhiladelphia
Period25/02/254/03/25
Internet address

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