Jian Feng*, Tian Liu, Cailing Du
CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2895-2909, 2024, DOI:10.32604/cmc.2024.056434
- 18 November 2024
Abstract Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph representation learning to eliminate the dependence of labels. However, existing studies neglect positional information when learning discrete snapshots, resulting in insufficient network topology learning. At the same time, due to the lack of appropriate data augmentation methods, it is difficult to capture the evolving patterns of the network effectively. To address the above problems, a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic graphs. Firstly, the global snapshot is built based on the historical snapshots… More >
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