Authors:
Daniele Maccagnola
;
Elisabetta Fersini
;
Rabah Djennadi
and
Enza Messina
Affiliation:
University of Milano-Bicocca, Italy
Keyword(s):
Community Detection, Social Network Analysis, Kernel Communities.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
Community Detection is a fundamental task in the field of Social Network Analysis, extensively studied in
literature. Recently, some approaches have been proposed to detect communities distinguishing their members
between kernel that represents opinion leaders, and auxiliary who are not leaders but are linked to them.
However, these approaches suffer from two important limitations: first, they cannot identify overlapping communities,
which are often found in social networks (users are likely to belong to multiple groups simultaneously);
second, they cannot deal with node attributes, which can provide important information related to
community affiliation. In this paper we propose a method to improve a well-known kernel-based approach
named Greedy-WeBA (Wang et al., 2011) and overcome these limitations. We perform a comparative analysis
on three social network datasets, Wikipedia, Twitter and Facebook, showing that modeling overlapping
communities and considering node attribu
tes strongly improves the ability of detecting real social network
communities.
(More)