Temporal Network Embedding with Micro- and Macro-dynamics Yuanfu Lu Xiao Wang Chuan Shi∗ Beijing University of Posts and Beijing University of Posts and Beijing University of Posts and Communications Communications Communications Beijing, China Beijing, China Beijing, China
[email protected] [email protected] [email protected] Philip S. Yu Yanfang Ye University of Illinois at Chicago Case Western Reserve University IL, USA OH, USA
[email protected] [email protected] ABSTRACT ACM Reference Format: Network embedding aims to embed nodes into a low-dimensional Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, and Yanfang Ye. 2019. Tem- space, while capturing the network structures and properties. Al- poral Network Embedding with Micro- and Macro-dynamics. In The 28th ACM International Conference on Information and Knowledge Management though quite a few promising network embedding methods have (CIKM ’19), November 3–7, 2019, Beijing, China. ACM, New York, NY, USA, been proposed, most of them focus on static networks. In fact, 10 pages. https://doi.org/10.1145/3357384.3357943 temporal networks, which usually evolve over time in terms of microscopic and macroscopic dynamics, are ubiquitous. The micro- 1 INTRODUCTION dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution Network embedding has shed a light on network analysis due pattern of the network scale. Both micro- and macro-dynamics to its capability of encoding the structures and properties of net- are the key factors to network evolution; however, how to ele- works with latent representations [1, 3].