CFGAT: A Coarse-To-Fine Graph Attention Network for Semi-supervised Node Classification

In this paper, we propose a novel semi-supervised graph node classification algorithm called Coarse-To-Fine Graph Attention Network (CFGAT), which can hierarchically enhance node representation ability in a coarse to fine manner. Specifically, CFGAT consists of two subnets: CoarseNet and FineNet. For the CoarseNet, we present a simple-yet-nontrivial node information coarsening strategy, which can generate coarse-grained features for all nodes on the graph by performing average on the structure-similar neighborhood information within densely-connected subgraphs. For the FineNet, the coarse-grained features obtained from the CoarseNet can be refined level by level using multiple reformulated graph attention layers. In addition, we also propose a Node-wise Receptive Field Selection Module which performs an adaptive receptive field selection for each node on the graph by assigning different attentions to different-scale node features extracted from multiple layers of the network. All proposed sub-Algorithms can be integrated into an overall framework and trained in an end-To-end manner. Experimental results on three commonly-used datasets demonstrate the effectiveness and superiority of the proposed framework.