Dynamic Detection of Communities and Their Evolutions in Temporal Social Networks

Authors

  • Yaowei Huang Shanghai Jiao Tong University
  • Jinghuan Shang Shanghai Jiao Tong University
  • Bill Lin Shanghai Jiao Tong University
  • Luoyi Fu Shanghai Jiao Tong University
  • Xinbing Wang Shanghai Jiao Tong University

Keywords:

Social Network, Community Detection, Temporal Social Network, Community Evolution

Abstract

In this paper, we propose a novel community detection model, which explores the dynamic community evolutions in temporal social networks by modeling temporal affiliation strength between users and communities. Instead of transforming dynamic networks into static networks, our model utilizes normal distribution to estimate the change of affiliation strength more concisely and comprehensively. Extensive quantitative and qualitative evaluation on large social network datasets shows that our model achieves improvements in terms of prediction accuracy and reveals distinctive insight about evolutions of temporal social networks.

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Published

2018-04-29

How to Cite

Huang, Y., Shang, J., Lin, B., Fu, L., & Wang, X. (2018). Dynamic Detection of Communities and Their Evolutions in Temporal Social Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12128