Graph Adversarial Diffusion Convolution

• URL: http://arxiv.org/abs/2406.02059v1
• Date: Tue, 4 Jun 2024 07:43:04 GMT
• Title: Graph Adversarial Diffusion Convolution
• Authors: Songtao Liu, Jinghui Chen, Tianfan Fu, Lu Lin, Marinka Zitnik, Dinghao Wu,
• Abstract summary: This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem. We derive a new variant of the Graph Diffusion Convolution architecture, called Graph Adversarial Diffusion Convolution (GADC)
• Score: 49.974206213411904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem. In this formulation, we first maximize the second term of GSD by introducing perturbations to the graph structure based on Laplacian distance and then minimize the overall loss of the GSD. By solving the min-max optimization problem, we derive a new variant of the Graph Diffusion Convolution (GDC) architecture, called Graph Adversarial Diffusion Convolution (GADC). GADC differs from GDC by incorporating an additional term that enhances robustness against adversarial attacks on the graph structure and noise in node features. Moreover, GADC improves the performance of GDC on heterophilic graphs. Extensive experiments demonstrate the effectiveness of GADC across various datasets. Code is available at https://github.com/SongtaoLiu0823/GADC.

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