A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks

• Published in: IEEE transactions on cybernetics Vol. 53; no. 6; pp. 3599 - 3612
• Main Authors: Gao, Chao, Zhu, Junyou, Zhang, Fan, Wang, Zhen, Li, Xuelong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Artificial neural networks; Clustering; Deep learning; Dynamic characteristics; Dynamic graphs; graph convolutional networks (GCNs); graph representation learning; Graph representations; Graphical representations; Graphs; Machine learning; Marine vehicles; Mutual information; mutual information (MI); Nodes; Performance evaluation; Proteins; Representation learning; Task analysis; Training
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2022.3159661

Graph representation learning has re-emerged as a fascinating research topic due to the successful application of graph convolutional networks (GCNs) for graphs and inspires various downstream tasks, such as node classification and link prediction. Nevertheless, existing GCN-based methods for graph...