Facetnet: a framework for analyzing communities and their evolutions in dynamic networks

By Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, Belle L. Tseng. The Web Conference, 2008

443 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9e5fc9fa7244f112c83b9a7f9079ee2a3fceb3db

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Citations in the corpus

Community detection in graphs (2009, methodology): “Based on evolutionary clustering, Lin et al. (Lin et al., 2008) introduced a framework, called FacetNet, that allows vertices to belong to more communities at the same time.”
Analyzing communities and their evolutions in dynamic social networks (2009, methodology): “3 The Model for P(Wt|Ut) For P (Wt |Ut), we adopt a stochastic block model first proposed in Yu et al. [2005]. Assume there exist m communities at time t and these m communities introduce a probability φt;i j for the interaction between nodes i and j by using a mixture model.”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, methodology): “Lin et al. (2008, 2009a) extended the graph-factorization clustering (GFC) and proposed the FacetNet algorithm for analyzing dynamic communities.”
Community Discovery in Dynamic Networks (2017, unknown): “Methods in this category: (Crane and Dempsey, 2015; Folino and Pizzuti, 2010; Gong et al. , 2012; G¨orke et al. , 2013; Kawadia and Sreenivasan, 2012; Lin et al. , 2008, 2009; Sun et al. , 2010; Tang et al. , 2008; Yang et al. , 2009; Zhou et al. , 2007) Refer to Appendix B.3 for description of…”
Community detection in Social Media (2012, methodology): “Finally, FacetNet (Lin et al. 2008) presents another important paradigm of incremental community detection formulated on the basis of evolutionary clustering (Chakrabarti et al.”
Towards more systematic Twitter analysis: metrics for tweeting activities (2013, methodology): “However, social network analysis is often used only to generate static snapshots while neglecting the network ’ s dynamics (Bruns, 2011; Lin, Chi, Zhu, Sundaram, & Tseng, 2008).”
A classification for community discovery methods in complex networks (2011, background): “The first is FacetNet [66], in which a framework to evaluate the evolution of the communities is developed.”
Community detection in networks: A user guide (2016, background): “Some interesting algorithms based on evolutionary clustering have been proposed (Chi et al., 2007; Lin et al., 2008).”
Tracking the Evolution of Communities in Dynamic Social Networks (2010, background): “Lin et al. [1] adapted two well-known toy networks to produce additional time step networks using membership switching and corresponding changes to node edges.”