Detecting communities and their evolutions in dynamic social networks—a Bayesian approach

By Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, Rong Jin. Machine-mediated learning, 2011

365 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/95f2692d9883ca388cb94426efed1a900c09f0db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. Philip S. Yu
2. Charu C. Aggarwal
3. Benjamin C. M. Fung
4. Ke Wang
5. Rui Chen
6. Ling Liu
7. Keke Chen
8. Jaideep Vaidya

In Statistical and Nonlinear Physics

1. Philip S. Yu
2. Shirui Pan
3. Catherine Matias
4. Vincent Miele

Citations in the corpus

• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021, methodology): “DynamicSBM (2010)[55] Propose a novel Bayesian approach for network tomographic inference buliding on MMSB and apply in dynamic network.”
• Community Discovery in Dynamic Networks (2017, neutral): “Methods in this category: (Herlau et al. , 2013; Ishiguro et al. , 2010; Matias et al. , 2015; Xu and Hero, 2014; Yang et al. , 2009, 2011) Refer to Appendix C.3 for description of methods.”
• Statistical clustering of temporal networks through a dynamic stochastic block model (2015, methodology): “Therefore, we do not only decipher evolving communities (such as in Yang et al., 2011) but we also highlight the dynamics of aloneness inside this class.”