AI RESEARCH
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
arXiv CS.LG
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ArXi:2305.09958v5 Announce Type: replace Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts of heterophilous GNNs incorporate long-range or global aggregations to distinguish nodes in the graph. However, these aggregations usually require iteratively maintaining and updating full-graph information, which limits their efficiency when applying to large-scale graphs.