Generative Models for Relational Structures
published: Sept. 13, 2010, recorded: August 2010, views: 2907
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 Gibbs sampling. 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. Empirical evaluations on real data reveal the practical utility of our proposed algorithm and show that our generative model gives good graph classification results.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !