New Insights into Graph Convolutional Networks using Neural Tangent Kernels

Understand

Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data.

• Although empirically successful, GCNs exhibit certain behaviour that has no rigorous explanation -- for instance, the performance of GCNs significantly degrades with increasing network depth, whereas it improves marginally with depth using skip connections.
• This paper focuses on semi-supervised learning on graphs, and explains the above observations through the lens of Neural Tangent Kernels (NTKs).
• We derive NTKs corresponding to infinitely wide GCNs (with and without skip connections).