Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View

Details Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View

2006-08-09

arxiv.org/abs/cs/0608050v1

Computer Science

Data Structures and Algorithms

12 Pages, 4 figures

Scientific paper

Dense sub-graphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Most existing community detection algorithms produce a hierarchical structure of community and seek a partition into communities that optimizes a given quality function. We propose new methods to improve the results of any of these algorithms. First we show how to optimize a general class of additive quality functions (containing the modularity, the performance, and a new similarity based quality function we propose) over a larger set of partitions than the classical methods. Moreover, we define new multi-scale quality functions which make it possible to detect the different scales at which meaningful community structures appear, while classical approaches find only one partition.

Affiliated with

Also associated with

No associations

Rating

Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View 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 Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-247452