Motif-Preserving Temporal Network Embedding

Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif --- triad --- and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction, and link recommendation.

Authors

• Hong Huang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology, Wuhan, 430074, China
• Zixuan Fang
• Xiao Wang MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics
• Youshan Miao
• Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab, School of Computer Science and Technology, HUST

Keywords

• Data Mining: Mining Graphs, Semi Structured Data, Complex Data
• Data Mining: Mining Spatial, Temporal Data