poster

Probabilistic model for discovering topic based communities in social networks

Authors:

Mrinmaya Sachan,

Danish Contractor,

Tanveer Faruquie,

Venkata SubramaniamAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2349 - 2352

https://doi.org/10.1145/2063576.2063963

Published: 24 October 2011 Publication History

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Abstract

Social graphs have received renewed interest as a research topic with the advent of social networking websites. These online networks provide a rich source of data to study user relationships and interaction patterns on a large scale. In this paper, we propose a generative Bayesian model for extracting latent communities from a social graph. We assume that community memberships depend on topics of interest between users and the link relationships between them in the social graph topology. In addition, we make use of the nature of interaction to gauge user interests. Our model allows communities to be related to multiple topics and each user in the graph can be a member of multiple communities. This gives an insight into user interests and topical distribution in communities. We show the effectiveness of our model using a real world data set and also compare our model with existing community discovery methods.

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Cited By

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Xu QQiu LLin RTang YHe CYuan C(2021)An Improved Community Detection Algorithm via Fusing Topology and Attribute Information2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437681(1069-1074)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437681
Xin YYang JXie ZZhang J(2015)An overlapping semantic community detection algorithm base on the ARTs multiple sampling modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.11.02942:7(3420-3432)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.11.029
Yu XYang JXie Z(2015)A semantic overlapping community detection algorithm based on field samplingExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.07.00942:1(366-375)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.07.009
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Index Terms

Probabilistic model for discovering topic based communities in social networks
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Probability and statistics

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Information

Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2011

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Xu QQiu LLin RTang YHe CYuan C(2021)An Improved Community Detection Algorithm via Fusing Topology and Attribute Information2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437681(1069-1074)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437681
Xin YYang JXie ZZhang J(2015)An overlapping semantic community detection algorithm base on the ARTs multiple sampling modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.11.02942:7(3420-3432)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.11.029
Yu XYang JXie Z(2015)A semantic overlapping community detection algorithm based on field samplingExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.07.00942:1(366-375)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.07.009
Zang WZhou CWang XGuo L(2014)Topic BlockProceedings of the 2014 15th International Conference on Parallel and Distributed Computing, Applications and Technologies10.1109/PDCAT.2014.33(159-165)Online publication date: 9-Dec-2014
https://dl.acm.org/doi/10.1109/PDCAT.2014.33
Correa DSureka APundir MVarde ASuchanek F(2012)iTopProceedings of the 5th Ph.D. workshop on Information and knowledge10.1145/2389686.2389698(51-58)Online publication date: 2-Nov-2012
https://dl.acm.org/doi/10.1145/2389686.2389698
Sachan MContractor DFaruquie TSubramaniam LMille AGandon FMisselis JRabinovich MStaab S(2012)Using content and interactions for discovering communities in social networksProceedings of the 21st international conference on World Wide Web10.1145/2187836.2187882(331-340)Online publication date: 16-Apr-2012
https://dl.acm.org/doi/10.1145/2187836.2187882

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Using content and interactions for discovering communities in social networks

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