Abstract
We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including MCMC maximum likelihood estimation algorithms. We discuss models of this type and give examples, as well as a demonstration of their use for hypothesis testing and classification. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Hanneke, S., & Xing, E. P. (2007). Discrete temporal models of social networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4503 LNCS, pp. 115–125). Springer Verlag. https://doi.org/10.1007/978-3-540-73133-7_9
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