Addressing the Limitations of Graph Convolutional Networks

Explore the challenges and potential solutions for Graph Convolutional Networks (GCNs) in this 28-minute DS4DM Coffee Talk presented by Sitao Luan from McGill University. Delve into the world of graph-structured problems and learn how GCN-based approaches have made significant strides in solving complex issues. Examine the limitations of GCNs, including the over-smoothing problem that hinders deep GCNs from fully utilizing multi-scale information, and the heterophily problem that causes graph-aware models to underperform compared to graph-agnostic models. Gain insights into proposed methods for addressing these challenges and discover current research directions in the field of graph convolutional neural networks.

On Addressing the Limitations of Graph Convolutional Networks, Sitao Luan