AI RESEARCH
Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs
arXiv CS.LG
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ArXi:2606.01660v1 Announce Type: new 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 complex hop aggregators do not reliably outperform simpler ones: on many benchmarks, a plain MLP-based aggregator matches or beats hop-attention variants. We revisit this behavior from a graph-filter perspective.