Towards democratic group detection in complex networks

Michele Coscia, Fosca Giannotti, Dino Pedreschi

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

Résumé

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.

langue originaleAnglais
titreSocial Computing, Behavioral-Cultural Modeling and Prediction, 5th International Conference, SBP 2012, Proceedings
Pages105-113
Nombre de pages9
Volume7227 LNCS
Les DOIs
Etat de la publicationPublié - 2012
Modification externeOui
Evénement5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012 - College Park, MD, États-Unis
Durée: 3 avr. 20125 avr. 2012

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7227 LNCS
ISSN (imprimé)03029743
ISSN (Electronique)16113349

Une conférence

Une conférence5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2012
Pays/TerritoireÉtats-Unis
La villeCollege Park, MD
période3/04/125/04/12

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