Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (6): 1420-1430.doi: 10.1007/s11390-020-0142-x

Special Issue: Artificial Intelligence and Pattern Recognition

• Regular Paper • Previous Articles     Next Articles

Pre-Train and Learn: Preserving Global Information for Graph Neural Networks

Dan-Hao Zhu1,2, Xin-Yu Dai2,*, Member, CCF, and Jia-Jun Chen2        

  1. 1 Library, Jiangsu Police Institute, Nanjing 210031, China;
    2 Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China
  • Received:2019-10-30 Revised:2020-10-09 Online:2021-11-30 Published:2021-12-01
  • Contact: Xin-Yu Dai
  • Supported by:
    This work was partially supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 18kJB510010, the Social Science Foundation of Jiangsu Province of China under Grant No. 19TQD002, and the National Nature Science Foundation of China under Grant No. 61976114.

Graph neural networks (GNNs) have shown great power in learning on graphs. However, it is still a challenge for GNNs to model information faraway from the source node. The ability to preserve global information can enhance graph representation and hence improve classification precision. In the paper, we propose a new learning framework named G-GNN (Global information for GNN) to address the challenge. First, the global structure and global attribute features of each node are obtained via unsupervised pre-training, and those global features preserve the global information associated with the node. Then, using the pre-trained global features and the raw attributes of the graph, a set of parallel kernel GNNs is used to learn different aspects from these heterogeneous features. Any general GNN can be used as a kernal and easily obtain the ability of preserving global information, without having to alter their own algorithms. Extensive experiments have shown that state-of-the-art models, e.g., GCN, GAT, Graphsage and APPNP, can achieve improvement with G-GNN on three standard evaluation datasets. Specially, we establish new benchmark precision records on Cora (84.31%) and Pubmed (80.95%) when learning on attributed graphs.

Key words: graph neural network; network embedding; representation learning; global information pre-train;

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