A curved exponential family model for complex networks |
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Authors: | Mark S Handcock Martina Morris |
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Institution: | 1. Department of Statistics, University of Washington, Seattle, USA
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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. |
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