PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Identifying Graph Clusters using Variational Inference and links to Covariance Parameterisation
David Barber
Royal Society Philosophical Transactions A 2008.


including Social Network, Web and molecular interaction analyses. From a computational viewpoint, nding these clusters or graph communities is a dicult problem. We consider the framework of Clique Matrices to decompose a graph into a set of possibly overlapping clusters, dened as well-connected subsets of vertices. The decomposition is based on a statistical description which encourages clusters to be well connected and few in number. The formal intractability of inferring the clusters is addressed using a variational approximation which has links to mean- eld theories in statistical mechanics. Clique matrices also play a natural role in parameterising positive denite matrices under zero constraints on elements of the matrix. We show that clique matrices can parameterise all positive denite matrices restricted according to a decomposable graph and form a structured Factor Analysis approximation in the non-decomposable case.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5332
Deposited By:David Barber
Deposited On:24 March 2009