Model‐based clustering for social networks

By Mark S. Handcock, Adrian E. Raftery, Jeremy Tantrum. 2007

851 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Graph Learning

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Citations in the corpus

Revisiting Semi-Supervised Learning with Graph Embeddings (2016, methodology): “A clustering method (Handcock et al., 2007) was proposed to learn latent social states in a social network to predict social ties.”
Community detection in graphs (2009, methodology): “Bayesian inference is frequently used in the analysis and modeling of real graphs, including social (Handcock et al., 2007; Koskinen and Snijders, 2007; Rhodes and Keefe, 2007) and biological networks (Berg and Lässig, 2006; Rowicka and Kudlicki, 2004).”
Dynamic mixed membership blockmodel for evolving networks (2009, background): “Dynamic network tomography can lead to important insights to the robustness of network structures, the cause and consequence of information di.usion, and Appearing in Proceedings of the 26 th International Conference on Machine Learning, Montreal, Canada, 2009.”
Community Detection and Mining in Social Media (2010, methodology): “A latent space model maps nodes in a network into a low-dimensional Euclidean space such that the proximity between nodes based on network connectivity are kept in the new space (Hoff et al., 2002; Handcock et al., 2007), then the nodes are clustered in the low-dimensional space using methods like k”
A state-space mixed membership blockmodel for dynamic network tomography (2008, background): “The latent space models (LSM) instead project nodes onto a latent space, where their similarities can be visualized and explored [Hoﬀ, Raftery and Handcock (2002); Hoﬀ (2003); Handcock, Raftery and Tantrum (2007)].”
Matrix estimation by Universal Singular Value Thresholding (2012, background): “There have been various attempts to estimate parameters in the latent space models (e.g. [55, 53, 4]).”
Discrete Temporal Models of Social Networks (2006, background): “nk and Strauss, 1986; Wasserman and Robins, 2005; Snijders, 2002; Robins and Pattison, 2005), which are descriptive in nature, latent space models that aim towards clustering and community discovery (Handcock and Raftery, 2007), and mixed-membership block models for role discovery (Airoldi et al., 2”
Community detection in networks: A user guide (2016, methodology): “Other techniques to extract the number of groups have been proposed (Côme and Latouche, 2015; Daudin et al., 2008; Handcock et al., 2007; Latouche et al., 2012; Newman and Reinert, 2016).”
Pseudo-likelihood methods for community detection in large sparse networks (2012, background): “These include the popular stochastic block model [20], its extensions to include varying degree distributions within communities [22] and overlapping communities [2, 4], and various latent variable models [17, 19].”
Yes, there is a correlation: - from social networks to personal behavior on the web (2008, background): “For exam ple, Handcock and Raftery’s [11] model for social networks incor porates assumptions about transitivity in link structure (if A is connected to B and B is connected to C, then A is likely connected t o”
Mixed Membership Stochastic Blockmodels (2007, methodology): “Here, we analyze a friendship network among the students, at the same school that was considered by Handcock et al. (2007) and discussants.”