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.
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Hanneke, S., Xing, E.P. (2007). Discrete Temporal Models of Social Networks. In: Airoldi, E., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds) Statistical Network Analysis: Models, Issues, and New Directions. ICML 2006. Lecture Notes in Computer Science, vol 4503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73133-7_9
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DOI: https://doi.org/10.1007/978-3-540-73133-7_9
Publisher Name: Springer, Berlin, Heidelberg
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