Discrete Temporal Models of Social Networks

By Steve Hanneke, Eric P. Xing. SNA@ICML, 2006

578 citations recorded by the source corpus.

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.

Subfields: Mathematical Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3779bd4298c6fc47c1262f345a361159e035ce0c

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In Mathematical Physics

In Statistical and Nonlinear Physics

Citations in the corpus

Recovering time-varying networks of dependencies in social and biological studies (2009, background): “Recently, a new class of models known as the temporal exponential random graph model (tERGM) has been proposed for modeling networks evolving over discrete time steps (5).”
Complex Dependencies in the Alliance Network (2012, background): “Specifically, in the international conflict network, they hypothesize that the network will contain few triangles in which A is at war with B, B with C, and A with C. Simply speaking, it would be redundant and inefficient for two countries at war with the same third country to fight each other.”
Time-Varying Dynamic Bayesian Networks (2009, background): “Recently, there has been a surge of interests along this direction [5, 6, 7, 8, 9, 10].”
Toward a Network Theory of Alliance Formation (2012, unknown): “D ow nl oa de d by [S ky le r C ra nm er ] a t 1 0: 46 3 0 M ay 2 01 2 Toward a Network Theory of Alliance Formation 301 Some empirical work has noted the network attributes of alliances.”
Inferential Network Analysis with Exponential Random Graph Models (2011, methodology): “Because the network of interest is observed longitudinally, we apply a longitudinal extension to the ERGM called the temporal Exponential Random Graph Model (TERGM), developed by Hanneke and Xing (2007) and extended by Desmarais and Cranmer (2010).”
A Survey of Statistical Network Models (2009, methodology): “Computation of the gradient and Hessian requires the mean and covariance of the sequence network statistics, which are exactly computable for a pair of networks, but require Gibbs sampling in the K-sequence case [139].”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “Especially Xing and his associates have recently introduced temporal exponential random graph models and time-varying networks to capture dynamics of networks (Hanneke and Xing 2006; Guo et al. 2007; Kolar et al. 2010).”