Discrete Temporal Models of Social Networks

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.

• Publication year

  2006

• Venue

  SNA@ICML

• Publication date

  2006-06-29

• Fields of study

  Mathematics, Computer Science, Sociology

• Identifiers
  DOI 10.1007/978-3-540-73133-7_9arXiv 0908.1258
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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