Cross Message Passing Graph Neural Network

Abstract

Most Graph Convolutional Networks (GCNs) used for graph classification task can fit into the Message Passing Neural Networks (MPNNs) framework. However, traditional MPNNs don't consider global information in the message passing phase in which the node embeddings are updated. In this paper, we propose a new model called Cross Message Passing Graph Neural Network (CMPGNN). The new model consists of two message passing phases, of which one is the local message passing phase used for node embeddings updating and another is the global message passing phase used for graph feature updating. Several convolutional layers are stacked together in the local message passing phase, in which different convolutional layers are used to update the node embeddings at different time steps. Each convolutional layer updates node embeddings not only according to the outputs calculated at the previous convolutional layer but also to the graph feature calculated at the previous time step. A readout layer shared by all time steps is used in the global message passing phase. At each time step, after the node embeddings are updated, the readout layer aggregates the embeddings of all nodes by using a global gated network, and feeds the aggregation result into a Gated Recurrent Unit (GRU) to update the graph feature.The above two message passing phases are executed alternatively. After all time steps, the graph feature obtained by the readout layer is fed into a MultiLayer perception for graph classification. We evaluate the performance of CMPGNN on 6 graph classification datasets. Experimental results show that compared with other 10 baselines, CMPGNN achieves the highest accuracy on 4 of the 6 benchmark datasets.