On the Experimental Transferability of Spectral Graph Convolutional Networks

Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studying the experimental transferability of spectral GCNs for a particular family of spectral graph networks using Chebyshev polynomials. This work introduces two contributions. First, numerical experiments exhibit good performances on two graph benchmarks, on tasks involving batches of graphs, namely graph regression, graph classification and node classification problems. Secondly we study a form of data augmentation through structural edge dropout showing performance improvements for GCNs. This work contributes to open research with public implementations of all experiments, enabling full reproducibility.