A dynamic modularity based community detection algorithm for large-scale networks: DSLM

Riza Aktunc; Ismail Hakki Toroslu; Mert Ozer; Hasan Davulcu

doi:10.1145/2808797.2808822

A dynamic modularity based community detection algorithm for large-scale networks: DSLM

Riza Aktunc, Ismail Hakki Toroslu, Mert Ozer, Hasan Davulcu

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

34 Scopus citations

Abstract

In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.

Original language	English (US)
Title of host publication	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Publisher	Association for Computing Machinery, Inc
Pages	1177-1183
Number of pages	7
ISBN (Print)	9781450338547
DOIs	https://doi.org/10.1145/2808797.2808822
State	Published - Aug 25 2015
Event	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: Aug 25 2015 → Aug 28 2015

Other

Other	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Country/Territory	France
City	Paris
Period	8/25/15 → 8/28/15

ASJC Scopus subject areas

Computer Science Applications
Computer Networks and Communications

Access to Document

10.1145/2808797.2808822

Cite this

Aktunc, R., Toroslu, I. H., Ozer, M., & Davulcu, H. (2015). A dynamic modularity based community detection algorithm for large-scale networks: DSLM. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 1177-1183). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2808822

A dynamic modularity based community detection algorithm for large-scale networks: DSLM. / Aktunc, Riza; Toroslu, Ismail Hakki; Ozer, Mert et al.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, 2015. p. 1177-1183.

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

Aktunc, R, Toroslu, IH, Ozer, M & Davulcu, H 2015, A dynamic modularity based community detection algorithm for large-scale networks: DSLM. in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, pp. 1177-1183, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 8/25/15. https://doi.org/10.1145/2808797.2808822

@inproceedings{031bc9831d754c2daa1d9f47d86730d7,

title = "A dynamic modularity based community detection algorithm for large-scale networks: DSLM",

abstract = "In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.",

author = "Riza Aktunc and Toroslu, {Ismail Hakki} and Mert Ozer and Hasan Davulcu",

year = "2015",

month = aug,

day = "25",

doi = "10.1145/2808797.2808822",

language = "English (US)",

isbn = "9781450338547",

pages = "1177--1183",

booktitle = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",

publisher = "Association for Computing Machinery, Inc",

note = "IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

}

TY - GEN

T1 - A dynamic modularity based community detection algorithm for large-scale networks

T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

AU - Aktunc, Riza

AU - Toroslu, Ismail Hakki

AU - Ozer, Mert

AU - Davulcu, Hasan

PY - 2015/8/25

Y1 - 2015/8/25

N2 - In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.

AB - In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.

UR - http://www.scopus.com/inward/record.url?scp=84962526616&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962526616&partnerID=8YFLogxK

U2 - 10.1145/2808797.2808822

DO - 10.1145/2808797.2808822

M3 - Conference contribution

AN - SCOPUS:84962526616

SN - 9781450338547

SP - 1177

EP - 1183

BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

PB - Association for Computing Machinery, Inc

Y2 - 25 August 2015 through 28 August 2015

ER -

A dynamic modularity based community detection algorithm for large-scale networks: DSLM

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this