Assessing degeneracy in statistical models of social networks
by Prof. Mark S. Handcock
Abstract: In this talk, recent advances in the statistical modeling of random graphs that have an impact on the empirical study of social networks are discussed. Statistical exponential family models (Wasserman & Pattison, 1996) are a generalization of the Markov random graph models introduced by Frank and Strauss (1986) which in turn are derived from developments in spatial statistics (Besag 1974). These models recognize the complex dependencies within relational data structures. A major barrier to the application of random graph models to social networks has been the lack of a sound statistical theory to evaluate model fit. This problem has at least three aspects: the specification of realistic models; the algorithmic difficulties of the inferential methods; and the assessment of the degree to which the graph structure produced by the models matches that of the data. These and related issues of model degeneracy and inferential degeneracy for commonly used estimators are discussed.
For More Information: Emma Lockwood tel: 8344 1617 email: email@example.com