DEDGCN: Dual Evolving Dynamic Graph Convolutional Network. (10th May 2022)

• Record Type: Journal Article
• Authors: Zhong, Fengzhe; Liu, Yan; Liu, Lian; Zhang, Guangsheng; Duan, Shunran
• Other Names: Wang Wei Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-05-10
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/6945397
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : With the wide application of graph data in many fields, the research of graph representation learning technology has become the focus of scholars' attention. Especially, dynamic graph representation learning is an important part of solving the problem of change graph in reality. On the one hand, most dynamic graph representation methods focus either on graph structure changes or node embedding changes, ignoring the internal relationship. On the other hand, most dynamic graph neural networks require learn node embeddings from specific tasks, resulting in poor universality of node embeddings and cannot be used in unsupervised tasks. Hence, Dual Evolving Dynamic Graph Convolutional Network (DEDGCN) was proposed to solve the above problems. DEDGCN uses the recurrent neural network to push the evolvement of GCN and nodes, from which it can extract the structural features of dynamic graph and learns the stability features of nodes, respectively, forming an adaptive dynamic graph convolution network. DEDGCN can be classified as unsupervised graph convolutional network. Thus, it is capable of training the unlabeled dynamic graph, it has more extensive application scenarios, and the calculated node embedding has strong generality. We evaluate our proposed method on experimental data in three tasks which are node classification, edge classification, and link prediction. In the classification task, facing the graph with large scale, complex connection relationship, and uncertain change rule, the F1 value of node classification task obtained by DEDGCN reaches 77%, and the F1 value of edge classification task reaches more than 90%. The results show that DEDGCN is effective in capturing graph features, and the effect of DEDGCN is much higher than other baseline methods, which proves the importance of capturing node stability features in dynamic graph representation learning. At the same time, the ability of DEDGCN in unsupervised tasks is further verified by using clustering and anomaly detection tasks, which proves that DEDGCN learning network embedding is widely used.