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A curved exponential family model for complex networks

Author

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  • Mark S. Handcock

    (University of Washington)

  • Martina Morris

    (University of Washington)

Abstract

Networks are being increasingly used to represent relational data. As the patterns of relations tends to be complex, many probabilistic models have been proposed to capture the structural properties of the process that generated the networks. Two features of network phenomena not captured by the simplest models is the variation in the number of relations individual entities have and the clustering of their relations. In this paper we present a statistical model within the curved exponential family class that can represent both arbitrary degree distributions and an average clustering coefficient. We present two tunable parameterizations of the model and give their interpretation. We also present a Markov Chain Monte Carlo (MCMC) algorithm that can be used to generate networks from this model.

Suggested Citation

  • Mark S. Handcock & Martina Morris, 2009. "A curved exponential family model for complex networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 294-302, December.
    • Handle: RePEc:spr:comaot:v:15:y:2009:i:4:d:10.1007_s10588-008-9055-x
    DOI: 10.1007/s10588-008-9055-x
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    Cited by:

    1. Yu Zhang & Yu Wu, 2012. "How behaviors spread in dynamic social networks," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 419-444, December.

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