1. CFGAT: A Coarse-to-Fine Graph Attention Network for Semi-supervised Node Classification
- Author
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Jin Fusheng, Dongmei Cui, Rong-Hua Li, and Guoren Wang
- Subjects
Computer science ,business.industry ,Feature extraction ,Pattern recognition ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Graph ,Coarse to fine ,Statistical classification ,Graph Node ,020204 information systems ,Attention network ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,Artificial intelligence ,business ,0105 earth and related environmental sciences - Abstract
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.
- Published
- 2020