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An Information Theoretic Approach to Learning Generative Graph Prototypes

An Information Theoretic Approach to Learning Generative Graph Prototypes

This video was recorded at 1st International Workshop on Similarity-Based Pattern Analysis and Recognition. 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... Show More
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