article

Community detection for emerging social networks

Authors:

Junyuan XieAuthors Info & Claims

World Wide Web, Volume 20, Issue 6

Pages 1409 - 1441

https://doi.org/10.1007/s11280-017-0441-5

Published: 01 November 2017 Publication History

Abstract

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the "New Network Community Detection" problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

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Cited By

Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615084(2208-2218)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615084
Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)CANA: Causal-enhanced Social Network AlignmentProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614799(2219-2228)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614799
Shao JWang YGao HShi BShen HCheng X(2023)AsyLinkNeurocomputing10.1016/j.neucom.2022.10.027515:C(174-184)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.10.027
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Index Terms

Community detection for emerging social networks
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image World Wide Web

World Wide Web Volume 20, Issue 6

November 2017

387 pages

ISSN:1386-145X

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Copyright © Copyright © 2017 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2017

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Cited By

Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)TOAK: A Topology-oriented Attack Strategy for Degrading User Identity Linkage in Cross-network LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615084(2208-2218)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615084
Shao JWang YGuo FShi BShen HCheng XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)CANA: Causal-enhanced Social Network AlignmentProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614799(2219-2228)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614799
Shao JWang YGao HShi BShen HCheng X(2023)AsyLinkNeurocomputing10.1016/j.neucom.2022.10.027515:C(174-184)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.10.027
Zeng WTang RWang HChen XWang W(2021)User Identification Based on Integrating Multiple User Information across Online Social NetworksSecurity and Communication Networks10.1155/2021/55334172021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5533417
Shao JWang YGao HShen HLi YCheng XDemartini GZuccon GCulpepper JHuang ZTong H(2021)Locate Who You AreProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482134(3413-3417)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482134
Tang ZTang YLi CCao JChen GLin R(2021)A fast local community detection algorithm in complex networksWorld Wide Web10.1007/s11280-021-00931-124:6(1929-1955)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11280-021-00931-1

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