Pooling Method Based on Edge Contraction for Graph Convolution Networks

In recent years, various graph pooling methods have been proposed, and the existing edge pooling methods have some problems. Edge pooling aggregates nodes by removing edges while considering some node characteristics. However, edge pooling ignores the surrounding node features and graph topology. We propose a novel graph pooling method to address this problem. To address the problem, we build a reasonable pooling graph topology, consider the structure and feature information of the graph, improve the objectivity of node selection, and use the edge pooling method to select edges after considering the structure and feature information of the graph. Experimental results on the dataset show that our method is effective in graph classification and outperforms state-of-the-art graph pooling methods.