PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Block Model Suitable for Sparse Graphs
Juuso Parkkinen, Janne Sinkkonen, Adam Gyenge and Samuel Kaski
In: Proceedings of the 7th International Workshop on Mining and Learning with Graphs (MLG 2009) (2009) KU Leuven .

Abstract

We introduce a new generative block model for graphs. Vertices (nodes) have mixed memberships in margin components, and edges arise from a multinomial defined over the cartesian product of the margin components. The model is able to represent block structures of “non-community” type, that is, it is able to model linkage between margin components. Compared to earlier mixed membership stochastic blockmodels which have a Bernoulli parameterization for the generation of links between each margin component pair, in the new model collapsed Gibbs samplers need to represent only those interactions with realized data in them, making possible large and sparse block models.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6296
Deposited By:Samuel Kaski
Deposited On:08 March 2010