Research map / Graph learning / GNN architectures
GAMMA: Gated Multi-hop Message Passing for Homophily-Agnostic Node Representation in GNNs
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Proposes GAMMA, a gated multi-hop message passing GNN for node representation learning that aims to work across homophilous and heterophilous graphs.
Problem
GNNs rely on homophily, which fails on heterophilic graphs where same-class nodes lie in distant neighborhoods rather than adjacent ones.
Approach
Uses gated multi-hop aggregation over intermediate hop representations to expand the receptive field in a homophily-agnostic way; the exact gating mechanism is not described in the available abstract.
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