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B1675
Topic: Contributions on networks Title: Estimating exponential random graph models for large networks Authors:  Alberto Caimo - Dublin Institute of Technology (Ireland) [presenting]
Abstract: The exponential random graph model (ERGM) is a statistical model for analysing social networks. However, estimating ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of networks that can be fitted. Recently, it has been shown that conditional estimation can be used to estimate ERGM parameters by estimating parameters for smaller conditionally independent subsets of the network. Snowball sampling can be used to generate such subsets. A large number of relatively small samples can be estimated in parallel, taking advantage of parallel computing to allow estimation of much larger networks than previously possible.