PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Information Theoretic Approach to Learning Generative Graph Prototypes
Lin Han, Edwin Hancock and Richard Wilson
In: SIMBAD - Similarity-Based Pattern Recognition - First International Workshop, September 28-30, 2011, Venice, Italy.


We present a method for constructing a generative model for sets of graphs by adopting a minimum description length approach. The method is posed in terms of learning a generative supergraph model from which the new samples can be obtained by an appropriate sampling mechanism. We commence by constructing a probability distribution for the occurrence of nodes and edges over the supergraph. We encode the complexity of the supergraph using the von-Neumann entropy. A variant of EM algorithm is developed to minimize the description length criterion in which the node correspondences between the sample graphs and the supergraph are treated as missing data.The maximization step involves updating both the node correspondence information and the structure of supergraph using graduated assignment. In the experimental part, we demonstrate the practical utility of our proposed algorithm and show that our generative model gives good graph classification results. Besides, we show how to perform graph clustering with Jensen-Shannon kernel and generate new sample graphs.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:8561
Deposited By:Edwin Hancock
Deposited On:13 February 2012