ES-GNN : Generalizing Graph Neural Networks Beyond Homophily With Edge Splitting

While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic linking patterns, wherein adjacent nodes may sh...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - PP(2024) vom: 12. Sept.
1. Verfasser:	Guo, Jingwei (VerfasserIn)
Weitere Verfasser:	Huang, Kaizhu, Zhang, Rui, Yi, Xinping
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2024
Zugriff auf das übergeordnete Werk:	IEEE transactions on pattern analysis and machine intelligence
Schlagworte:	Journal Article