Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
<|reference_start|>Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach: 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.<|reference_end|>
@article{han2024node-wise,
    title={Node-wise Filtering in Graph Neural Networks: A Mixture of Experts
  Approach},
    author={Haoyu Han, Juanhui Li, Wei Huang, Xianfeng Tang, Hanqing Lu, Chen Luo,
  Hui Liu, Jiliang Tang},
    journal={arXiv preprint arXiv:2406.03464},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03464},
    primaryClass={cs.LG}
}