research-article

Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process

Authors:

Christopher C. YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 2

Article No.: 36, Pages 1 - 21

https://doi.org/10.1145/2517085

Published: 30 April 2014 Publication History

Abstract

Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many community network detection techniques employ a two-stage approach to identify and detect evolutionary relationships between communities of two adjacent time epochs. These techniques often identify communities with high temporal variation, since the two-stage approach detects communities of each epoch independently without considering the continuity of communities across two time epochs. Other techniques require identification of a predefined number of hidden communities which is not realistic in many applications. To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which enables the detection of hidden communities and tracks their evolution simultaneously from a network stream. The number of hidden communities is automatically determined by a temporal Dirichlet process without human intervention. We tested our proposed technique on three different testbeds with results identifying a high performance level when compared to the baseline algorithm.

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Index Terms

Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process
1. Information systems
  1. Information retrieval

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 2

Special Issue on Linking Social Granularity and Functions

April 2014

347 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2611448

Issue’s Table of Contents

Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2014

Accepted: 01 August 2013

Revised: 01 December 2012

Received: 01 July 2012

Published in TIST Volume 5, Issue 2

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Li KZeng H(2024)Multidisciplinary parameters for characterizing the 3D urban morphology: An overview based on the relational perspectiveSustainable Cities and Society10.1016/j.scs.2024.105364106(105364)Online publication date: Jul-2024
https://doi.org/10.1016/j.scs.2024.105364
Das SBiswas A(2024)TSInc: Tie strength based incremental community detection using information cascadesInternational Journal of Information Technology10.1007/s41870-024-01844-816:5(3243-3253)Online publication date: 17-Apr-2024
https://doi.org/10.1007/s41870-024-01844-8
Lu QDai HLi BLi PZhou QZhou QYang G(2024)EPCQ: Efficient Privacy-Preserving Contact Query Processing over Trajectory Data in CloudWeb and Big Data10.1007/978-981-97-7241-4_12(183-198)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7241-4_12
Poux-Médard GVelcin JLoudcher SChen HDuh WHuang HKato MMothe JPoblete B(2023)Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled NetworksProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591675(1569-1577)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591675
Jiao PLi TWu HWang CHe DWang W(2023)HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node LevelIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.314928534:11(8310-8323)Online publication date: Nov-2023
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Li TWang WJiao PWang YDing RWu HPan LJin D(2023)Exploring Temporal Community Structure via Network EmbeddingIEEE Transactions on Cybernetics10.1109/TCYB.2022.316834353:11(7021-7033)Online publication date: Nov-2023
https://doi.org/10.1109/TCYB.2022.3168343
Moscato VSperlì G(2022)Community detection over feature-rich information networksInformation Systems10.1016/j.is.2022.102092109:COnline publication date: 1-Nov-2022
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Zhao ZLi CZhang XChiclana FViedma E(2019)An incremental method to detect communities in dynamic evolving social networksKnowledge-Based Systems10.1016/j.knosys.2018.09.002163(404-415)Online publication date: Jan-2019
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