NDLI: Entropy based community detection in augmented social networks

Content Provider	IEEE Xplore Digital Library
Author	Cruz, J.D. Bothorel, C. Poulet, F.
Copyright Year	2011
Description	Author affiliation: LUSSI Department, Télécom - Bretagne, Brest - France (Cruz, J.D.; Bothorel, C.) \|\| IRISA, Université de Rennes I, France (Poulet, F.)
Abstract	Social network analysis has become a major subject in recent times, bringing also several challenges in the computer science field. One aspect of the social network analysis is the community detection problem, which can be seen as a graph clustering problem. However, social networks are more than a graph, they have an interesting amount of information derived from its social aspect, such as profile information, content sharing and annotations, among others. Most of the community detection algorithms use only the structure of the network, i.e., the graph. In this paper we propose a new method which uses the semantic information along with the network structure in the community detection process. Thus, our method combines an algorithm for optimizing modularity and an entropy-based data clustering algorithm, which tries to find a partition with low entropy and keeping in mind the modularity.
Starting Page	163
Ending Page	168
File Size	207515
Page Count	6
File Format	PDF
ISBN	9781457711329
e-ISBN	9781457711336
DOI	10.1109/CASON.2011.6085937
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-19
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Social network services Communities Semantics Social Networks Analysis Clustering algorithms Graph Clustering Community Detection Entropy Partitioning algorithms Optimization
Content Type	Text
Resource Type	Article

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