A Clustering Algorithm for Mining Overlapping Highly Connected Subgraphs

In this paper, we give several properties related to highly connected graph. Based on these properties, we give a redefinition of highly connected subgraph which results in an algorithm for determining whether a given graph is highly connected in linear time. Then we present a computationally efficient algorithm, called MOHCS, for mining overlapping highly connected subgraphs. We experimentally evaluate the performance of MOHCS using a variety of real and synthetic data sets. Our results show that MOHCS is effective in finding overlapping highly connected subgraphs both in computer- generated graph and yeast protein network.