On the NP-Completeness of Some Graph Cluster Measures

Computer Science – Computational Complexity

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

9 pages, no figures

Scientific paper

Graph clustering is the problem of identifying sparsely connected dense subgraphs (clusters) in a given graph. Proposed clustering algorithms usually optimize various fitness functions that measure the quality of a cluster within the graph. Examples of such cluster measures include the conductance, the local and relative densities, and single cluster editing. We prove that the decision problems associated with the optimization tasks of finding the clusters that are optimal with respect to these fitness measures are NP-complete.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

On the NP-Completeness of Some Graph Cluster Measures 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 On the NP-Completeness of Some Graph Cluster Measures, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On the NP-Completeness of Some Graph Cluster Measures will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-84581

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.