An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

Research on graph classification tasks based on graph neural networks has attracted wide attention. The graphs to be classified may have various graph sizes (i.e., different numbers of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient). The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters. Consequently, it is unreasonable to learn graph representation from a set of diverse graphs by a unified graph neural network. Inspired by this, we design an end-to-end Multiplex Graph Neural Network (MxGNN) that learns graph representations with multiple GNNs, and combines them with a learnable method. The main challenge lies with the combination of multiple representation results. Our new findings show that the a priori graph properties do have an effect on the quality of representation learning, which can be used to guide the learning. Our experiments on graph classification with multiple data sets show that the performance of MxGNN is better than the existing graph representation learning methods.