  Graph neural networks (GNNs), which propagate the node features through the
edges and learn how to transform the aggregated features under label
supervision, have achieved great success in supervised feature extraction for
both node-level and graph-level classification tasks. However, GNNs typically
treat the graph structure as given and ignore how the edges are formed. This
paper introduces a graph generative process to model how the observed edges are
generated by aggregating the node interactions over a set of overlapping node
communities, each of which contributes to the edges via a logical OR mechanism.
Based on this generative model, we partition each edge into the summation of
multiple community-specific weighted edges and use them to define
community-specific GNNs. A variational inference framework is proposed to
jointly learn a GNN-based inference network that partitions the edges into
different communities, these community-specific GNNs, and a GNN-based predictor
that combines community-specific GNNs for the end classification task.
Extensive evaluations on real-world graph datasets have verified the
effectiveness of the proposed method in learning discriminative representations
for both node-level and graph-level classification tasks.
