Community finding within the community set space

Authors:
Jerry Scripps;Christian Trefftz
Affiliations:
Grand Valley State University, Allendale, MI;Grand Valley State University, Allendale, MI
Venue:
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Year:
2013

Citing 5
Cited 0

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Structural inference of hierarchies in networks

ICML'06 Proceedings of the 2006 conference on Statistical network analysis
A multi-resolution approach to learning with overlapping communities

Proceedings of the First Workshop on Social Media Analytics
Exploring the Community Set Space

WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Quantified Score

Hi-index	0.00

Visualization

Abstract

Community finding algorithms strive to find communities that have a higher connectivity within the communities than between them. Recently a framework called the community set space was introduced which provided a way to measure the quality of community sets. We present a new community finding algorithm, CHI, designed to minimize the violations defined by this framework. It will be shown that the CHI algorithm has similarities to kmeans. It is flexible and fast and can also be tuned to find certain types of communities. It is optimized for the community set framework and results so that it performs better than other algorithms within that framework.