Discrete Temporal Models of Social Networks

ICML 2006 pp. 115-125

doi:10.1007/978-3-540-73133-7_9 /icml/2006/hanneke2006icml-discrete/

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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Text

Hanneke and Xing. "Discrete Temporal Models of Social Networks." International Conference on Machine Learning, 2006. doi:10.1007/978-3-540-73133-7_9

Markdown

[Hanneke and Xing. "Discrete Temporal Models of Social Networks." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/hanneke2006icml-discrete/) doi:10.1007/978-3-540-73133-7_9

BibTeX

@inproceedings{hanneke2006icml-discrete,
  title     = {{Discrete Temporal Models of Social Networks}},
  author    = {Hanneke, Steve and Xing, Eric P.},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {115-125},
  doi       = {10.1007/978-3-540-73133-7_9},
  url       = {https://mlanthology.org/icml/2006/hanneke2006icml-discrete/}
}