Node-centric detection of overlapping communities in social networks

Yehonatan Cohen; Danny Hendler; Amir Rubin

doi:10.1109/ASONAM.2016.7752423

Node-centric detection of overlapping communities in social networks

Yehonatan Cohen, Danny Hendler, Amir Rubin

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.

Original language	American English
Title of host publication	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Editors	Ravi Kumar, James Caverlee, Hanghang Tong
Pages	1384-1385
Number of pages	2
ISBN (Electronic)	9781509028467
DOIs	https://doi.org/10.1109/ASONAM.2016.7752423
State	Published - 21 Nov 2016
Event	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: 18 Aug 2016 → 21 Aug 2016

Conference

Conference	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/Territory	United States
City	San Francisco
Period	18/08/16 → 21/08/16

Keywords

Community detection
Louvain method
modularity
objective function
overlapping communities

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Sociology and Political Science
Communication

Access to Document

10.1109/ASONAM.2016.7752423

Cite this

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title = "Node-centric detection of overlapping communities in social networks",

abstract = "We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.",

keywords = "Community detection, Louvain method, modularity, objective function, overlapping communities",

author = "Yehonatan Cohen and Danny Hendler and Amir Rubin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",

year = "2016",

month = nov,

day = "21",

doi = "10.1109/ASONAM.2016.7752423",

language = "American English",

pages = "1384--1385",

editor = "Ravi Kumar and James Caverlee and Hanghang Tong",

booktitle = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",

}

Cohen, Y, Hendler, D & Rubin, A 2016, Node-centric detection of overlapping communities in social networks. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752423, pp. 1384-1385, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 18/08/16. https://doi.org/10.1109/ASONAM.2016.7752423

Node-centric detection of overlapping communities in social networks. / Cohen, Yehonatan; Hendler, Danny; Rubin, Amir.
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. 2016. p. 1384-1385 7752423.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Node-centric detection of overlapping communities in social networks

AU - Cohen, Yehonatan

AU - Hendler, Danny

AU - Rubin, Amir

PY - 2016/11/21

Y1 - 2016/11/21

N2 - We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.

AB - We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.

KW - Community detection

KW - Louvain method

KW - modularity

KW - objective function

KW - overlapping communities

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U2 - 10.1109/ASONAM.2016.7752423

DO - 10.1109/ASONAM.2016.7752423

M3 - Conference contribution

SP - 1384

EP - 1385

BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

A2 - Kumar, Ravi

A2 - Caverlee, James

A2 - Tong, Hanghang

T2 - 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Y2 - 18 August 2016 through 21 August 2016

ER -

Node-centric detection of overlapping communities in social networks

Abstract

Conference

Keywords

All Science Journal Classification (ASJC) codes

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