Recent high-throughput experimental methods have generated protein-protein interaction data in the genome scale, called interactome. Various graph clustering algorithms have been applied to the protein interactome networks for identifying protein complexes and predicting functional modules. Although the previous algorithms are scalable and robust, their accuracy is still limited because of complex connectivity of the networks. In this study, we propose a novel information-theoretic definition, Graph Entropy, as a measure of structural complexity of a graph. Loss of graph entropy represents an increase in modularity of the graph. Based on this concept, we present a graph clustering algorithm. Starting from a random seed vertex and its neighbors as a seed cluster, the algorithm iteratively adds or removes vertices on the border of the cluster to minimize graph entropy. We make an additional improvement on the algorithm for generating overlapping clusters. In the experiments with the yeast protein interactome network, we show the graph entropy-based approach has higher accuracy in predicting functional modules than other competing methods.
['Hao Lian', 'Chengsen Song', 'Young-Rae Cho']
Decomposing protein interactome networks by graph entropy