Node Classification On Non Homophilic
The task of node classification on non-homophilous graphs (heterophilous graphs) aims to evaluate the performance of models specifically designed for heterogeneous datasets. This task focuses on graphs where edges between different classes are more common than edges within the same class, and through systematic testing and analysis, it reveals differences in how models perform when dealing with heterophilous graphs, providing crucial references for optimizing graph neural networks.
Chameleon (48%/32%/20% fixed splits)
Chameleon(60%/20%/20% random splits)
ACM-GCN+
Cornell (48%/32%/20% fixed splits)
Cornell (60%/20%/20% random splits)
ACMII-GCN
Deezer-Europe
ACMII-GCN+++
Film(48%/32%/20% fixed splits)
genius
ClenshawGCN
Penn94
Pubmed
Squirrel (48%/32%/20% fixed splits)
Texas (48%/32%/20% fixed splits)
Texas(60%/20%/20% random splits)
twitch-gamers
Wisconsin (48%/32%/20% fixed splits)
O(d)-NSD
Wisconsin(60%/20%/20% random splits)
ACM-GCN++