5 months ago

Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks

Abstract

Official

pytorch

Mentioned in GitHub

Benchmark	Methodology	Metrics
node-classification-on-actor	GGCN	Accuracy: 37.54 ± 1.56
node-classification-on-chameleon	GGCN	Accuracy: 71.14 ± 1.84
node-classification-on-citeseer-48-32-20	GGCN	1:1 Accuracy: 77.14 ± 1.45
node-classification-on-cora-48-32-20-fixed	GGCN	1:1 Accuracy: 87.95 ± 1.05
node-classification-on-cornell	GGCN	Accuracy: 85.68 ± 6.63
node-classification-on-non-homophilic-10	GGCN	1:1 Accuracy: 37.54 ± 1.56
node-classification-on-non-homophilic-10	GPRGCN	1:1 Accuracy: 35.16 ± 0.9
node-classification-on-non-homophilic-11	GGCN	1:1 Accuracy: 71.14 ±1.84
node-classification-on-non-homophilic-12	GGCN	1:1 Accuracy: 55.17 ± 1.58
node-classification-on-non-homophilic-7	GGCN	1:1 Accuracy: 85.68 ± 6.63
node-classification-on-non-homophilic-8	GGCN	1:1 Accuracy: 86.86 ± 3.29
node-classification-on-non-homophilic-9	GGCN	1:1 Accuracy: 84.86 ± 4.55
node-classification-on-pubmed-48-32-20-fixed	GGCN	1:1 Accuracy: 89.15 ± 0.37
node-classification-on-squirrel	GGCN	Accuracy: 55.17 ± 1.58
node-classification-on-texas	GGCN	Accuracy: 84.86 ± 4.55
node-classification-on-wisconsin	GGCN	Accuracy: 86.86 ± 3.29

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