Towards democratic group detection in complex networks

Michele Coscia, Fosca Giannotti, Dino Pedreschi

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

To detect groups in networks is an interesting problem with applications in social and security analysis. Many large networks lack a global community organization. In these cases, traditional partitioning algorithms fail to detect a hidden modular structure, assuming a global modular organization. We define a prototype for a simple local-first approach to community discovery, namely the democratic vote of each node for the communities in its ego neighborhood. We create a preliminary test of this intuition against the state-of-the-art community discovery methods, and find that our new method outperforms them in the quality of the obtained groups, evaluated using metadata of two real world networks. We give also the intuition of the incremental nature and the limited time complexity of the proposed algorithm.

Original languageEnglish
Title of host publicationSocial Computing, Behavioral-Cultural Modeling and Prediction, 5th International Conference, SBP 2012, Proceedings
Pages105-113
Number of pages9
Volume7227 LNCS
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012 - College Park, MD, United States
Duration: 3 Apr 20125 Apr 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7227 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012
Country/TerritoryUnited States
CityCollege Park, MD
Period3/04/125/04/12

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