Node-Level Adaptive Graph Convolutional Neural Network for Node Classification Tasks
WANG Xinlong1, HU Rui1, GUO Yaliang1, DU Hangyuan1, ZHANG Binqi3, WANG Wenjian2,3
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006; 2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006; 3. Department of Network Security, Shanxi Police College, Tai-yuan 030401
Abstract：Graph neural networks learn node embeddings by recursively sampling and aggregating information from nodes in a graph. However, the relatively fixed pattern of existing methods in node sampling and aggregation results in inadequate capture of local pattern diversity, thereby degrading the performance of the model. To solve this problem, a node-level adaptive graph convolutional neural network(NA-GCN) is proposed. A sampling strategy based on node importance is designed to adaptively determine the neighborhood size of each node. An aggregation strategy based on the self-attention mechanism is presented to adaptively fuse the node information within a given neighborhood. Experimental results on multiple benchmark graph datasets show the superiority of NA-GCN in node classification tasks.
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