Overlapping Kernel-based Community Detection with Node Attributes

Daniele Maccagnola, Elisabetta Fersini, Rabah Djennadi, Enza Messina

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 attributes strongly improves the ability of detecting real social network communities.

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Paper Citation


in Harvard Style

Maccagnola D., Fersini E., Djennadi R. and Messina E. (2015). Overlapping Kernel-based Community Detection with Node Attributes . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 517-524. DOI: 10.5220/0005640205170524


in Bibtex Style

@conference{kdir15,
author={Daniele Maccagnola and Elisabetta Fersini and Rabah Djennadi and Enza Messina},
title={Overlapping Kernel-based Community Detection with Node Attributes},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={517-524},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005640205170524},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Overlapping Kernel-based Community Detection with Node Attributes
SN - 978-989-758-158-8
AU - Maccagnola D.
AU - Fersini E.
AU - Djennadi R.
AU - Messina E.
PY - 2015
SP - 517
EP - 524
DO - 10.5220/0005640205170524