Network In Graph Neural Network

no code implementations • 23 Nov 2021 • Xiang Song, Runjie Ma, Jiahang Li, Muhan Zhang, David Paul Wipf

However, wider hidden layers can easily lead to overfitting, and incrementally adding more GNN layers can potentially result in over-smoothing. In this paper, we present a model-agnostic methodology, namely Network In Graph Neural Network (NGNN ), that allows arbitrary GNN models to increase their model capacity by making the model deeper.

Fraud Detection Link Prediction +1