Hierarchical Multiview Top-k Pooling With Deep-Q-Networks
Zhipeng Li, Hailong Su, Yong Wu, Qinhu Zhang, Changan Yuan, Valeriya Gribova, Vladimir Filaretov, De-Shuang Huang
Abstract
Graph Neural Networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on Graph Convolutional Neural Networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single-view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the Hierarchical Multi-view Top-k Pooling with Deep Q-networks (HMTPool), which scores nodes taking into account multi-view information (Considering graph structure and features) and does not rely on the empirical adaptive selection of the best k-value. HMTPool is a two-stage process. It first uses a variant GCN and MLP to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multi-view information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a Deep-Q-Network-based Top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUDatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach.