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

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arxiv 2406.03464 v1 pith:N4FSLJQR submitted 2024-06-05 cs.LG

Haoyu Han , Juanhui Li , Wei Huang , Xianfeng Tang , Hanqing Lu , Chen Luo , Hui Liu , Jiliang Tang This is my paper
classification cs.LG
keywords filtergraphsglobalgraphheterophilichomophilicpatternsapproach
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs

    cs.LG 2026-06 conditional novelty 7.0 of 10

    FilterMoE uses joint node-channel routing of Chebyshev filter experts through a 3D gating tensor in pre-propagation GNNs and outperforms baselines on nine of eleven benchmarks while ranking first on all three large-sc...

  2. Closed-Form Node Classification with Exact Graph Unlearning

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    Closed-form Ridge and LCF-Net predictors match 2-layer GNN performance on node classification while supporting exact graph unlearning with K-hop locality and large speedups.

  3. Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification

    cs.LG 2026-04 unverdicted novelty 5.0 of 10

    D2MoE dynamically allocates expert resources in graph MoEs via difficulty-driven top-p routing based on predictive entropy, yielding higher accuracy and lower memory/time costs on node classification benchmarks.

  4. Retrieval-Augmented Generation with Graphs (GraphRAG)

    cs.IR 2024-12 unverdicted novelty 5.0 of 10

    A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.

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