MOHCS: Towards Mining Overlapping Highly Connected Subgraphs

Details MOHCS: Towards Mining Overlapping Highly Connected Subgraphs MOHCS: Towards Mining Overlapping Highly Connected Subgraphs

2008-06-19

arxiv.org/abs/0806.3215v1

Computer Science

Distributed, Parallel, and Cluster Computing

Scientific paper

Many networks in real-life typically contain parts in which some nodes are more highly connected to each other than the other nodes of the network. The collection of such nodes are usually called clusters, communities, cohesive groups or modules. In graph terminology, it is called highly connected graph. In this paper, we first prove some properties related to highly connected graph. Based on these properties, we then redefine the highly connected subgraph which results in an algorithm that determines 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 have evaluated experimentally the performance of MOHCS using real and synthetic data sets from computer-generated graph and yeast protein network. Our results show that MOHCS is effective and reliable in finding overlapping highly connected subgraphs. Keywords-component; Highly connected subgraph, clustering algorithms, minimum cut, minimum degree

Affiliated with

Also associated with

No associations

Rating

MOHCS: Towards Mining Overlapping Highly Connected Subgraphs does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with MOHCS: Towards Mining Overlapping Highly Connected Subgraphs, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and MOHCS: Towards Mining Overlapping Highly Connected Subgraphs will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-563325