Fast Inference for Model-Based Clustering of Networks Using an Approximate Case-Control Likelihood

• Activity Number: 209
• Type: Invited
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: International Indian Statistical Association
• Author(s): Adrian Raftery*+ and Xiaoyue Niu and Peter Hoff and Ka Yee Yeung
• Companies: University of Washington and University of Washington and University of Washington and University of Washington
• Keywords: clustering ; genome science ; graph ; Markov chain Mone Carlo ; protein-protein interaction ; social network

Abstract

The model-based clustering latent space network model represents relational data visually and takes account of several basic network properties. Due to the structure of its likelihood function, the computational cost is of order O(n^2), where n is the number of nodes. This makes it infeasible for large networks. We propose an approximation of the log likelihood function. We adapt the case-control idea from epidemiology and construct an approximate case-control likelihood which is an unbiased estimator of the full likelihood. Replacing the full likelihood by the case-control likelihood in the MCMC estimation of the latent space model reduces the computational time from O(n^2) to O(n), making it feasible for large networks. We evaluate its performance using simulated and real data. We fit the model to a large protein-protein interaction data using the case-control likelihood and use the model fitted link probabilities to identify false positive links.