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FilterMoE enhances PPGNNs with joint node-channel adaptive filtering

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new approach for pre-propagation graph neural networks (PPGNNs) called FilterMoE. This method addresses the puzzle of why more complex aggregators don't always outperform simpler ones in PPGNNs. FilterMoE introduces a mixture-of-experts design that routes Chebyshev filter experts jointly over nodes and channels, outperforming existing PPGNNs on nine out of eleven benchmarks. AI

IMPACT Introduces a novel routing mechanism for graph neural networks, potentially improving performance on various graph-based tasks.

RANK_REASON The cluster contains a research paper detailing a new method for graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FilterMoE enhances PPGNNs with joint node-channel adaptive filtering

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The cluster contains a research paper detailing a new method for graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zichao Yue, Zhiru Zhang ·

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

    arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop features, which makes them highly scalable. A puzzle in this regime is that more…

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