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arXiv · 2406.03464

Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

Haoyu Han·Juanhui Li·Wei Huang·Xianfeng Tang·Hanqing Lu·Chen Luo·Hui Liu·Jiliang Tang
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Abstract

Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global filter, typically a low-pass filter for homophilic graphs and a high-pass filter for heterophilic graphs. However, real-world graphs often exhibit a complex mix of homophilic and heterophilic patterns, rendering a single global filter approach suboptimal. In this work, we theoretically demonstrate that a global filter optimized for one pattern can adversely affect performance on nodes with differing patterns. To address this, we introduce a novel GNN framework Node-MoE that utilizes a mixture of experts to adaptively select the appropriate filters for different nodes. Extensive experiments demonstrate the effectiveness of Node-MoE on both homophilic and heterophilic graphs.

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Haoyu Han, Juanhui Li, Wei Huang, Xianfeng Tang, Hanqing Lu, Chen Luo, Hui Liu, Jiliang Tang. 2024-06-05. Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach. https://arxiv.org/abs/2406.03464

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