  Spectral Graph Convolutional Networks (GCNs) are a generalization of
convolutional networks to learning on graph-structured data. Applications of
spectral GCNs have been successful, but limited to a few problems where the
graph is fixed, such as shape correspondence and node classification. In this
work, we address this limitation by revisiting a particular family of spectral
graph networks, Chebyshev GCNs, showing its efficacy in solving graph
classification tasks with a variable graph structure and size. Chebyshev GCNs
restrict graphs to have at most one edge between any pair of nodes. To this
end, we propose a novel multigraph network that learns from multi-relational
graphs. We model learned edges with abstract meaning and experiment with
different ways to fuse the representations extracted from annotated and learned
edges, achieving competitive results on a variety of chemical classification
benchmarks.
