  Graph Convolutional Neural Networks (Graph CNNs) are generalizations of
classical CNNs to handle graph data such as molecular data, point could and
social networks. Current filters in graph CNNs are built for fixed and shared
graph structure. However, for most real data, the graph structures varies in
both size and connectivity. The paper proposes a generalized and flexible graph
CNN taking data of arbitrary graph structure as input. In that way a
task-driven adaptive graph is learned for each graph data while training. To
efficiently learn the graph, a distance metric learning is proposed. Extensive
experiments on nine graph-structured datasets have demonstrated the superior
performance improvement on both convergence speed and predictive accuracy.
