Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909217656406016 |
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| author | Han, Haoyu Li, Juanhui Huang, Wei Tang, Xianfeng Lu, Hanqing Luo, Chen Liu, Hui Tang, Jiliang |
| author_facet | Han, Haoyu Li, Juanhui Huang, Wei Tang, Xianfeng Lu, Hanqing Luo, Chen Liu, Hui Tang, Jiliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03464 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach Han, Haoyu Li, Juanhui Huang, Wei Tang, Xianfeng Lu, Hanqing Luo, Chen Liu, Hui Tang, Jiliang Machine Learning 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. |
| title | Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.03464 |