Learning to Approximate Adaptive Kernel Convolution on Graphs
Jaeyoon Sim,
Sooyeon Jeon,
InJun Choi
et al.
Abstract:Various Graph Neural Networks (GNN) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes excessively averaged as the number of hidden layers increases. The issue stems from the intrinsic formulation of conventional graph convolution where the nodal features are aggregated from a direct neighborhood per layer across the entire nodes in the graph. As setting different number of hidden layers per node is infeasible, recent … Show more
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