Computer Science > Machine Learning
arXiv:2406.03464 (cs)
[Submitted on 5 Jun 2024]
Title:Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
View a PDF of the paper titled Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach, by Haoyu Han and 7 other authors
View PDFHTML (experimental)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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2406.03464 [cs.LG] |
| (orarXiv:2406.03464v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2406.03464 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach, by Haoyu Han and 7 other authors
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