Maximum likelihood estimation for social network dynamics

Tom A.B. Snijders, Johan Koskinen, Michael Schweinberger

Research output: Contribution to journalArticlepeer-review

104 Scopus citations

Abstract

A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, where the network dynamics can be interpreted as being generated by choices made by the social actors represented by the nodes of the graph. An algorithm for calculating the Maximum Likelihood estimator is presented, based on data augmentation and stochastic approximation. An application to an evolving friendship network is given and a small simulation study is presented which suggests that for small data sets the Maximum Likelihood estimator is more efficient than the earlier proposed Method of Moments estimator.

Original languageEnglish (US)
Pages (from-to)567-588
Number of pages22
JournalAnnals of Applied Statistics
Volume4
Issue number2
DOIs
StatePublished - Jun 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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