Graph convolutional networks with multi-level coarsening for graph classification

Graph convolutional networks (GCNs) have attracted increasing attention in recent years. Many important tasks in graph analysis involve graph classification which aims to map a graph to a certain category. However, as the number of convolutional layers increases, most existing GCNs suffer from the problem of over-smoothing, which makes it difficult to extract the hierarchical information and global patterns of graphs when learning its representations. In this paper, we propose a multi-level coarsening based GCN (MLC-GCN) for graph classification. Specifically, from the perspective of graph analysis, we develop new insights into the convolutional architecture of image classification. Inspired by this, the two-stage MLC-GCN architecture is presented. In the architecture, we first introduce an adaptive structural coarsening module to produce a series of coarsened graphs and then construct the convolutional network based on these graphs. In contrast to existing GCNs, MLC-GCN has the advantages of learning graph representations at multiple levels while preserving the local and global information of graphs. Experimental results on multiple benchmark datasets demonstrate that the proposed MLC-GCN method is competitive with the state-of-the-art graph classification methods.