Analyzing communities and their evolutions in dynamic social networks

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Community Detection and Mining in Social Media (2010, methodology): “A similar evolutionary clustering scheme has been instantiated with different community detection methods, including latent space models (Sarkar and Moore, 2005), probabilistic soft clustering (Lin et al., 2009), block model approximation (Yang et al.”
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): “parts of the communities. The stable community cores, less unstable than raw community partitions, can be more eciently tracked. Explicit smoothing. Some algorithms (e.g., (Folino and Pizzuti, 2014; Lin et al., 2009)) explicitly introduce smoothing in their denition, requiring the partition found at”