Dynamic social network analysis using latent space models

By Purnamrita Sarkar, Andrew W. Moore. SKDD, 2005

509 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2d507c3c24eaa0deaf68ba24d2a654b172ea770c

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Citations in the corpus

Dynamic Network Embedding by Modeling Triadic Closure Process (2018, methodology): “Most existing work, varying from Bayesian inference (Ho, Yin, and Xing 2016; Hoff, Raftery, and Handcock 2012) , multidimensional scaling (Sarkar and Moore 2005), matrix factorization (Erds, Gemulla, and Terzi 2014;”
Topics over time: a non-Markov continuous-time model of topical trends (2006, unknown): “In all cases, for simplicity, we ﬁx the number of topics T = 50 .”
A Survey of Statistical Network Models (2009, background): “7 we reproduce a graphic illustration of the inferred dynamic evolution of the network from [263].”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, background): “Sarkar and Moore (2005) proposed a dynamic method that embeds nodes into latent spaces where the locations of the nodes at consecutive timesteps are regularized so that dramatic change is unlikely.”
Social ties and their relevance to churn in mobile telecom networks (2008, background): “Recently, generalized models [30] have been proposed to capture changing relationships over time.”
Relational learning via latent social dimensions (2009, background): “graphs that serve the purpose of social dimension extraction, including graph partitioning [15], latent space model [12, 29], block model [28, 1], spectral clustering [37], etc.”
Community detection in networks: A user guide (2016, methodology): “An alternative way to uncover the evolution of communities by accounting for the correlation between configurations of neighbouring time intervals is to use probabilistic models (Peixoto, 2015a; Peixoto and Rosvall, 2015; Sarkar and Moore, 2005; Yang et al., 2009).”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, background): “The Dynamic Social Network in Latent space’’ (DSNL) model [70] is the first dynamic latent space model [31].”
The convergence of social and technological networks (2008, background): “16, 17, 18, 28, 37 Recent work by Salganik, Dodds, and Watts raises the interesting possibility that the outcomes of certain types of social-feedback effects may in fact be inherently unpredictable .”
Statistical Models for Social Networks (2011, background): “Snijders tained from Exponential Random Graph Models, and the importance attached to degree-related effects in stochastic actor-oriented models in Snijders et al. (2010). Models from the physics as well as statistical backgrounds are treated in depth by Kolaczyk (2009).”
Social Network Data Analytics (2011, background): “Lang and Rao [57] discuss a strategy for improving the conductance of any arbitrary bipartition or cut of the graph.”
Tensor Decompositions for temporal knowledge base completion (2020, methodology): “Temporal link prediction methods Sarkar & Moore (2006) describes a bayesian model and learning method for representing temporal relations.”
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.”
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (2020, methodology): “Temporal smoothness methods ensure embedding stability across consecutive time-steps [27, 40].”