Abstract #301057

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JSM 2003 Abstract #301057
Activity Number: 293
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301057
Title: Bayesian Models for Sparse Edge Weighted Directed Graphs
Author(s): Deepak K. Agarwal*+
Companies: AT&T Research Labs
Address: 180 Park Ave., Florham Park, NJ, 07932-1004,
Keywords: social networks ; random effects ; E-M algorithm
Abstract:

We propose a new class of models based on stochastic blockmodels that provide global measures for a directed graph based on local interactions. The models we implement differ from the ones that already exist in the literature that focus on very small (20-30 nodes) unweighted graphs that are not too sparse. Our models apply to large (200-300 nodes), extremely sparse weighted graphs. The issue of sparseness is tackled by using Bayesian models that are known to be computationally intensive. The models are fitted using an E-M algorithm, which has performed well so far. We illustrate our methodology by fitting the models to some subgraphs of a large telecommunications network.

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