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Using content and interactions for discovering communities in social networks

Authors:

Mrinmaya Sachan,

Danish Contractor,

Tanveer A. Faruquie,

L. Venkata SubramaniamAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 331 - 340

https://doi.org/10.1145/2187836.2187882

Published: 16 April 2012 Publication History

Abstract

In recent years, social networking sites have not only enabled people to connect with each other using social links but have also allowed them to share, communicate and interact over diverse geographical regions. Social network provide a rich source of heterogeneous data which can be exploited to discover previously unknown relationships and interests among groups of people. In this paper, we address the problem of discovering topically meaningful communities from a social network. We assume that a persons' membership in a community is conditioned on its social relationship, the type of interaction and the information communicated with other members of that community. We propose generative models that can discover communities based on the discussed topics, interaction types and the social connections among people. In our models a person can belong to multiple communities and a community can participate in multiple topics. This allows us to discover both community interests and user interests based on the information and linked associations. We demonstrate the effectiveness of our model on two real word data sets and show that it performs better than existing community discovery models.

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Cited By

Dubey SMeijers MSmit ESmit E(2024)Beyond climate change? Environmental discourse on the planetary boundaries in Twitter networksClimatic Change10.1007/s10584-024-03729-y177:5Online publication date: 25-Apr-2024
https://doi.org/10.1007/s10584-024-03729-y
Boutin RBouveyron CLatouche P(2023)Embedded topics in the stochastic block modelStatistics and Computing10.1007/s11222-023-10265-933:5Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11222-023-10265-9
Yang HZhang JDing XChen CWang L(2022)GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community DetectionEntropy10.3390/e2409127424:9(1274)Online publication date: 9-Sep-2022
https://doi.org/10.3390/e24091274
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Index Terms

Using content and interactions for discovering communities in social networks
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Dubey SMeijers MSmit ESmit E(2024)Beyond climate change? Environmental discourse on the planetary boundaries in Twitter networksClimatic Change10.1007/s10584-024-03729-y177:5Online publication date: 25-Apr-2024
https://doi.org/10.1007/s10584-024-03729-y
Boutin RBouveyron CLatouche P(2023)Embedded topics in the stochastic block modelStatistics and Computing10.1007/s11222-023-10265-933:5Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11222-023-10265-9
Yang HZhang JDing XChen CWang L(2022)GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community DetectionEntropy10.3390/e2409127424:9(1274)Online publication date: 9-Sep-2022
https://doi.org/10.3390/e24091274
Bansal MSharma D(2022)Density-based structural embedding for anomaly detection in dynamic networksNeurocomputing10.1016/j.neucom.2022.05.109500:C(724-740)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.05.109
Kumaran PChitrakala S(2022)Topic adaptive sentiment classification based community detection for social influential gauging in online social networksMultimedia Tools and Applications10.1007/s11042-021-11855-382:6(8943-8982)Online publication date: 25-Feb-2022
https://doi.org/10.1007/s11042-021-11855-3
Hailu YYing ZJin ZDeyun CGuanglu S(2021)Event-based Community Detection in Micro-Blog NetworksInternational Journal of Performability Engineering10.23940/ijpe.21.01.p6.607317:1(60)Online publication date: 2021
https://doi.org/10.23940/ijpe.21.01.p6.6073
Chen HYang CZhang XLiu ZSun MJin J(2021)From Symbols to Embeddings: A Tale of Two Representations in Computational Social ScienceJournal of Social Computing10.23919/JSC.2021.00112:2(103-156)Online publication date: Jun-2021
https://doi.org/10.23919/JSC.2021.0011
Saranya SRajalakshmi M(2021)Certain Strategic Study on Machine Learning-Based Graph Anomaly DetectionMobile Computing and Sustainable Informatics10.1007/978-981-16-1866-6_5(65-94)Online publication date: 23-Jul-2021
https://doi.org/10.1007/978-981-16-1866-6_5
Bhowmik KRalescu A(2021)Taking a Close Look at Twitter Communities and ClustersModelling and Development of Intelligent Systems10.1007/978-3-030-68527-0_12(187-201)Online publication date: 12-Feb-2021
https://doi.org/10.1007/978-3-030-68527-0_12
Wang CZhang HChen BWang DWang ZZhou MLarochelle HRanzato MHadsell RBalcan MLin H(2020)Deep relational topic modeling via graph poisson gamma belief networkProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3495766(488-500)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3495766
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