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Do We Need Anisotropic Graph Neural Networks?
[article]
2022
arXiv
pre-print
Common wisdom in the graph neural network (GNN) community dictates that anisotropic models – in which messages sent between nodes are a function of both the source and target node – are required to achieve state-of-the-art performance. Benchmarks to date have demonstrated that these models perform better than comparable isotropic models – where messages are a function of the source node only. In this work we provide empirical evidence challenging this narrative: we propose an isotropic GNN,
arXiv:2104.01481v5
fatcat:hzfr4th5lndxzniw3j6ua3mrlu