Revealing network communities through modularity maximization by a contraction-dilation method

Details Revealing network communities through modularity maximization by a contraction-dilation method Revealing network communities through modularity maximization by a contraction-dilation method

Apr 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009njph...11d3025m&link_type=abstract

New Journal of Physics, Volume 11, Issue 4, pp. 043025 (2009).

Physics

7

Scientific paper

Many real-world systems can be described by networks whose structures relate to functional properties. An important way to reveal topology-function correlations is to detect the community structures, which can be well evaluated by graph modularity. By maximizing modularity, large networks can be divided into naturally separated groups. Here, we propose a contraction-dilation algorithm based on single-node-move operations and a perturbation strategy. Tests on artificial and real-world networks show that the algorithm is efficient for discovering community structures with high modularity scores and accuracies at low expenses of both time and memory.

Affiliated with

Also associated with

No associations

Rating

Revealing network communities through modularity maximization by a contraction-dilation method 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 Revealing network communities through modularity maximization by a contraction-dilation method, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Revealing network communities through modularity maximization by a contraction-dilation method will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1864753