Graph Convolutional Network Representation Learning Method with Community-enhanced Temporal Features for Dynamic Graphs

Author:

Bi Qi1ORCID,Liu Jiayuan1ORCID,Tang Jiexing1ORCID

Affiliation:

1. school of computing, Xi'an University of Posts and Telecommunications, China

Publisher

ACM

Reference25 articles.

1. Nguyen G H, Lee J B, Rossi R A, Continuous-time dynamic network embeddings[C]//Companion proceedings of the the web conference 2018. 2018: 969-976.

2. Liu W, Li H, Xie B. Real-time graph partition and embedding of large network[C]//2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, 2018: 432-441.

3. High-order Proximity Preserved Embedding For Dynamic Networks

4. Zhou L,Yang Y,Ren X,et al. Dynamic network embedding by modeling triadic closure process[A]. Thirty-Second AAAI Conference on Artificial Intelligence [C].USA: AAAI,2018.571 - 578.

5. Anomaly detection in large-scale data stream networks

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