FacetNet: A Framework for Analyzing Communities and Their Evolutions in Dynamic Networks. We discover communities from social network data, and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals’ roles and social status in the network as well as changes to individuals’ research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. In this novel framework, communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions. As a result, this framework will discover communities that jointly maximize the fit to the observed data and the temporal evolution. Our approach relies on formulating the problem in terms of non-negative matrix factorization, where communities and their evolutions are factorized in a unified way. Then we develop an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution. We perform extensive experimental studies, on both synthetic datasets and real datasets, to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods.

References in zbMATH (referenced in 16 articles )

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  1. Dong, Shi: Improved label propagation algorithm for overlapping community detection (2020)
  2. Kim, Bomin; Lee, Kevin H.; Xue, Lingzhou; Niu, Xiaoyue: A review of dynamic network models with latent variables (2018)
  3. Wang, Peizhuo; Gao, Lin; Ma, Xiaoke: Dynamic community detection based on network structural perturbation and topological similarity (2017)
  4. Sarıyüce, Ahmet Erdem; Gedik, Buğra; Jacques-Silva, Gabriela; Wu, Kun-Lung; Çatalyürek, Ümit V.: SONIC: streaming overlapping community detection (2016)
  5. Dinh, Thang N.; Nguyen, Nam P.; Alim, Md Abdul; Thai, My T.: A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks (2015)
  6. Mankad, Shawn; Michailidis, George: Analysis of multiview legislative networks with structured matrix factorization: does Twitter influence translate to the real world? (2015)
  7. Aggarwal, Charu; Subbian, Karthik: Evolutionary network analysis: a survey (2014)
  8. Wang, Li; Wang, Jiang; Bi, Yuanjun; Wu, Weili; Xu, Wen; Lian, Biao: Noise-tolerance community detection and evolution in dynamic social networks (2014)
  9. Wu, Zhenyu; Zou, Ming: An incremental community detection method for social tagging systems using locality-sensitive hashing (2014) ioport
  10. Gong, Mao-Guo; Zhang, Ling-Jun; Ma, Jing-Jing; Jiao, Li-Cheng: Community detection in dynamic social networks based on multiobjective immune algorithm (2012)
  11. Papadopoulos, Symeon; Kompatsiaris, Yiannis; Vakali, Athena; Spyridonos, Ploutarchos: Community detection in social media performance and application considerations (2012) ioport
  12. Saganowski, Stanisław; Bródka, Piotr; Kazienko, Przemysław: Influence of the user importance measure on the group evolution discovery (2012)
  13. Tang, Lei; Wang, Xufei; Liu, Huan: Community detection via heterogeneous interaction analysis (2012) ioport
  14. Zheng, Xiaolong; Zhong, Yongguang; Zeng, Daniel; Wang, Fei-Yue: Social influence and spread dynamics in social networks (2012) ioport
  15. Coscia, Michele; Giannotti, Fosca; Pedreschi, Dino: A classification for community discovery methods in complex networks (2011)
  16. Yang, Tianbao; Chi, Yun; Zhu, Shenghuo; Gong, Yihong; Jin, Rong: Detecting communities and their evolutions in dynamic social networks -- a Bayesian approach (2011)