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Identification and Overidentification of Linear Structural Equation Models
Neural Information Processing Systems http://nips.cc/
0f956ca6f667c62e0f71511773c86a59-Supplemental-Conference.pdf
Weanalyzegraphsmoothingwith meanaggregation,whereeachnodesuccessively receives the average of the features of its neighbors. Indeed, it has quickly been observed that Graph Neural Networks (GNNs), which generally follow some variant of Message-Passing (MP) with repeated aggregation, may be subject to the oversmoothing phenomenon: by performing too many rounds of MP, the node features tend to converge to a non-informative limit. In the case of mean aggregation, forconnected graphs, thenodefeatures become constant across the whole graph.