  Graph neural networks (GNN) extends deep learning to graph-structure dataset.
Similar to Convolutional Neural Networks (CNN) using on image prediction,
convolutional and pooling layers are the foundation to success for GNN on graph
prediction tasks. In the initial PAN paper, it uses a path integral based graph
neural networks for graph prediction. Specifically, it uses a convolution
operation that involves every path linking the message sender and receiver with
learnable weights depending on the path length, which corresponds to the
maximal entropy random walk. It further generalizes such convolution operation
to a new transition matrix called maximal entropy transition (MET). Because the
diagonal entries of the MET matrix is directly related to the subgraph
centrality, it provide a trial mechanism for pooling based on centrality score.
While the initial PAN paper only considers node features. We further extends
its capability to handle complex heterogeneous graph including both node and
edge features.
