research-article

Community Discovery from Social Media by Low-Rank Matrix Recovery

Authors:

Jinfeng Zhuang,

Steven C. H. Hoi,

Xian-Sheng Hua,

Yongdong ZhangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 4

Article No.: 67, Pages 1 - 19

https://doi.org/10.1145/2668110

Published: 23 January 2015 Publication History

Abstract

The pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective generating process, the explicitly observed communities (e.g., group-user and user-user relationship) are often noisy and incomplete in nature. This paper presents a novel approach to discovering communities from social media, including the group membership and user friend structure, by exploring a low-rank matrix recovery technique. In particular, we take Flickr as one exemplary social media platform. We first model the observed indicator matrix of the Flickr community as a summation of a low-rank true matrix and a sparse error matrix. We then formulate an optimization problem by regularizing the true matrix to coincide with the available rich context and content (i.e., photos and their associated tags). An iterative algorithm is developed to recover the true community indicator matrix. The proposed approach leads to a variety of social applications, including community visualization, interest group refinement, friend suggestion, and influential user identification. The evaluations on a large-scale testbed, consisting of 4,919 Flickr users, 1,467 interest groups, and over five million photos, show that our approach opens a new yet effective perspective to solve social network problems with sparse learning technique. Despite being focused on Flickr, our technique can be applied in any other social media community.

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Cited By

Martinez-Castano RLosada DPichel J(2022)Real-Time Focused Extraction of Social Media UsersIEEE Access10.1109/ACCESS.2022.316897710(42607-42622)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3168977
Su YLi YBai XJing P(2020)Predicting the popularity of micro-videos via a feature-discrimination transductive modelMultimedia Systems10.1007/s00530-020-00660-xOnline publication date: 14-Jun-2020
https://doi.org/10.1007/s00530-020-00660-x
Nie LLiu MSong X(2019)Multimodal Learning toward Micro-Video UnderstandingSynthesis Lectures on Image, Video, and Multimedia Processing10.2200/S00938ED1V01Y201907IVM0209:4(1-186)Online publication date: 17-Sep-2019
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Index Terms

Community Discovery from Social Media by Low-Rank Matrix Recovery
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 4

Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

January 2015

390 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2699158

Editor:
Huan Liu
Arizona State University, USA

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Copyright © 2015 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 January 2015

Revised: 01 October 2014

Accepted: 01 January 2014

Received: 01 March 2013

Published in TIST Volume 5, Issue 4

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Cited By

Martinez-Castano RLosada DPichel J(2022)Real-Time Focused Extraction of Social Media UsersIEEE Access10.1109/ACCESS.2022.316897710(42607-42622)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3168977
Su YLi YBai XJing P(2020)Predicting the popularity of micro-videos via a feature-discrimination transductive modelMultimedia Systems10.1007/s00530-020-00660-xOnline publication date: 14-Jun-2020
https://doi.org/10.1007/s00530-020-00660-x
Nie LLiu MSong X(2019)Multimodal Learning toward Micro-Video UnderstandingSynthesis Lectures on Image, Video, and Multimedia Processing10.2200/S00938ED1V01Y201907IVM0209:4(1-186)Online publication date: 17-Sep-2019
https://doi.org/10.2200/S00938ED1V01Y201907IVM020
Jiang XWei SZhao RLiu RZhao YZhao Y(2019)A Visual Perspective for User Identification Based on Camera FingerprintImage and Graphics10.1007/978-3-030-34110-7_5(52-63)Online publication date: 23-Aug-2019
https://dl.acm.org/doi/10.1007/978-3-030-34110-7_5
Qiu LYu JLi JJia H(2018)Tibetan Weibo User Group Division Based on Semantic Information in the Era of Big DataComplexity10.1155/2018/58090432018Online publication date: 5-Aug-2018
https://dl.acm.org/doi/10.1155/2018/5809043
Jing PSu YNie LBai XLiu JWang M(2018)Low-Rank Multi-View Embedding Learning for Micro-Video Popularity PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.278578430:8(1519-1532)Online publication date: 1-Aug-2018
https://doi.org/10.1109/TKDE.2017.2785784
Mark BRaskutti GWillett R(2018)Network Estimation from Point Process DataIEEE Transactions on Information Theory10.1109/TIT.2018.2875766(1-1)Online publication date: 2018
https://doi.org/10.1109/TIT.2018.2875766
Wang MZuo WWang Y(2018)An improved density peaks-based clustering method for social circle discovery in social networksNeurocomputing10.1016/j.neucom.2015.11.091179:C(219-227)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2015.11.091
Cerqueti RFerraro GIovanella A(2018)A new measure for community structures through indirect social connectionsExpert Systems with Applications10.1016/j.eswa.2018.07.040114(196-209)Online publication date: Dec-2018
https://doi.org/10.1016/j.eswa.2018.07.040
Hu YZhao CCai DHe XLi X(2016)Atom Decomposition with Adaptive Basis Selection Strategy for Matrix CompletionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/290371612:3(1-25)Online publication date: 15-Jun-2016
https://dl.acm.org/doi/10.1145/2903716

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