A Bayesian mixture model for symmetric matrix data writes one precision matrix as a single power of the other; applied to GPS mobility, it links crime and youth employment to weekly activity patterns.
Discrete Temporal Models of Social Networks
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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 maximum likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.
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Bayesian Gaussian Mixture Modeling for Symmetric Matrix Variate Data
A Bayesian mixture model for symmetric matrix data writes one precision matrix as a single power of the other; applied to GPS mobility, it links crime and youth employment to weekly activity patterns.