DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node
  Feature Noise
Tai Hasegawa Sukwon Yun Xin Liu Yin Jun Phua Tsuyoshi Murata
Graph Neural Networks (GNNs) have achieved notable success in various
applications over graph data. However, recent research has revealed that
real-world graphs often contain noise, and GNNs are susceptible to noise in the
graph. To address this issue, several Graph Structure Learning (GSL) models
have been introduced. While GSL models are tailored to enhance robustness
against edge noise through edge reconstruction, a significant limitation
surfaces: their high reliance on node features. This inherent dependence
amplifies their susceptibility to noise within node features. Recognizing this
vulnerability, we present DEGNN, a novel GNN model designed to adeptly mitigate
noise in both edges and node features. The core idea of DEGNN is to design two
separate experts: an edge expert and a node feature expert. These experts
utilize self-supervised learning techniques to produce modified edges and node
features. Leveraging these modified representations, DEGNN subsequently
addresses downstream tasks, ensuring robustness against noise present in both
edges and node features of real-world graphs. Notably, the modification process
can be trained end-to-end, empowering DEGNN to adjust dynamically and achieves
optimal edge and node representations for specific tasks. Comprehensive
experiments demonstrate DEGNN's efficacy in managing noise, both in original
real-world graphs and in graphs with synthetic noise.