  Graph neural networks (GNN) has been demonstrated to be effective in
classifying graph structures. To further improve the graph representation
learning ability, hierarchical GNN has been explored. It leverages the
differentiable pooling to cluster nodes into fixed groups, and generates a
coarse-grained structure accompanied with the shrinking of the original graph.
However, such clustering would discard some graph information and achieve the
suboptimal results. It is because the node inherently has different
characteristics or roles, and two non-isomorphic graphs may have the same
coarse-grained structure that cannot be distinguished after pooling. To
compensate the loss caused by coarse-grained clustering and further advance
GNN, we propose a multi-channel graph convolutional networks (MuchGCN). It is
motivated by the convolutional neural networks, at which a series of channels
are encoded to preserve the comprehensive characteristics of the input image.
Thus, we define the specific graph convolutions to learn a series of graph
channels at each layer, and pool graphs iteratively to encode the hierarchical
structures. Experiments have been carefully carried out to demonstrate the
superiority of MuchGCN over the state-of-the-art graph classification
algorithms.
