EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks

1 code implementation • 27 May 2022 • Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei

Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels.

Node Classification