Title: Nonparametric models for structured sparse graphs
Authors: Sinead Williamson - University of Texas at Austin (United Kingdom) [presenting]
Abstract: There has been recent interest in the Bayesian community in models for sparse graphs with an unbounded number of vertices. Such models are appropriate for modeling large social or interaction networks, where the number of vertices scales approximately linearly with the number of interactions. However, sparsity is only one aspect of the structure of such networks, and naive sparse models tend to ignore the presence of locally dense sub-graphs and latent communities. We propose models appropriate for binary and integer-valued graphs that are globally sparse, but which contain locally dense sub-graphs, and show how these models can be used to infer latent communities from social network data.