Graph Reconfigurable Pooling for Graph Representation Learning

Abstract

In recent years, graph neural networks have been widely used for tasks such as graph classification, link prediction, and node classification, and have achieved excellent results. In order to apply GNNs to graph classification tasks, recent works generate graph-level representations using node representations through a hierarchical pooling approach. Existing graph pooling methods such as DiffPool and EigenPool encourage adjacent nodes to be assigned to the same cluster, making the node assignment process similar to the graph partitioning process that ignores the role of nodes or some substructures (e.g., amino acids) in the process of composing a graph (e.g., proteins). In this paper, we propose a new pooling operator RecPool to capture the role played by nodes in the process of composing a graph. Specifically, we probabilistically model the feature distribution of the coarsened graph, construct the feature distribution of each cluster, resample the features of the coarsened graph into the original nodes according to the soft assignment matrix, reconstruct the original graph, and optimize the soft assignment matrix to divide the nodes that play the same role in the reconstruction process into the same cluster. The excellent performance of Recpool is demonstrated through experiments on four public benchmark dataset.