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A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Overlapping Community Detection with Two-Level Expansion by Local Clustering Coefficients

Authors : Yi-Jen Su, Che-Chun Lee

Published in: Security with Intelligent Computing and Big-data Services

Publisher: Springer International Publishing

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Abstract

Community detection is crucial to Social Network Analysis (SNA) in that it helps to discover high-density overlapping communities hidden in complex networks for advanced applications. This study proposed a novel community detection method by seed set expansion. The method gathered meaningful nodes into a seed set, which was then used as a central node to merge neighbor nodes until communities were found. To enhance efficiency, a two-level expansion approach was further developed, which adopted the 80/20 rule and involved threshold change in order to discover cohesive subgroups of smaller sizes. To detect overlapping communities, local clustering coefficients (LCC) were calculated to measure the interaction density between neighbor nodes and determine whether they expanded or not. The experiment results were evaluated by measuring the cohesion quality of communities.

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Metadata
Title
Overlapping Community Detection with Two-Level Expansion by Local Clustering Coefficients
Authors
Yi-Jen Su
Che-Chun Lee
Copyright Year
2018
Book
Security with Intelligent Computing and Big-data Services

Print ISBN: 978-3-319-76450-4

Electronic ISBN: 978-3-319-76451-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-76451-1_11

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