research-article

Social-network analysis using topic models

Authors:

Junghoo ChoAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 565 - 574

https://doi.org/10.1145/2348283.2348360

Published: 12 August 2012 Publication History

Abstract

In this paper, we discuss how we can extend probabilistic topic models to analyze the relationship graph of popular social-network data, so that we can group or label the edges and nodes in the graph based on their topic similarity. In particular, we first apply the well-known Latent Dirichlet Allocation (LDA) model and its existing variants to the graph-labeling task and argue that the existing models do not handle popular nodes (nodes with many incoming edges) in the graph very well. We then propose possible extensions to this model to deal with popular nodes. Our experiments show that the proposed extensions are very effective in labeling popular nodes, showing significant improvements over the existing methods. Our proposed methods can be used for providing, for instance, more relevant friend recommendations within a social network.

References

[1]

Multinomial distribution. http://en.wikipedia.org/wiki/Multinomial_distribution.

[2]

E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. In J. Mach. Learn. Res., 2008.

Digital Library

[3]

D. M. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tanenbaum. Hierarchical topic models and the nested chinese restaurant process. In Neural Information Processing Systems, 2003.

[4]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.

Digital Library

[5]

K. R. Canini, L. Shi, and T. L. Griffiths. Online inference of topics with latent dirichlet allocation. In Artifical Intelligence and Statistics, 2009.

[6]

M. Ester, H. peter Kriegel, J. S, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, pages 226--231. AAAI Press, 1996.

Digital Library

[7]

M. Girolami and A. Kaban. On an equivalence between plsi and lda. In SIGIR, 2003.

Digital Library

[8]

M. Harvey, I. Ruthven, and M. J. Carman. Improving social bookmark search using personalised latent variable language models. In WSDM, 2011.

Digital Library

[9]

K. Henderson and T. Eliassi-Rad. Applying latent dirichlet allocation to group discovery in large graphs. In Proceedings of the 2009 ACM symposium on Applied Computing, 2009.

Digital Library

[10]

M. D. Hoffman, D. M. Blei, and F. Bach. Online learning for latent dirichlet allocation. In In NIPS, 2010.

[11]

T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, 1999.

Digital Library

[12]

T. Iwata, T. Yamada, and N. Ueda. Modeling social annotation data with content relevance using a topic model. In NIPS, 2009.

[13]

J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 1999.

Digital Library

[14]

H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, WWW '10, pages 591--600, New York, NY, USA, 2010. ACM.

Digital Library

[15]

S. Lloyd. Least squares quantization in pcm. IEEE Transactions on Information Theory, pages 129--137, 1982.

Digital Library

[16]

A. Mccallum, X. Wang, and A. Corrada-Emmanuel. Topic and role discovery in social networks with experiments on enron and academic email. Journal of Artificial Intelligence Research, 30:249--272, 2007.

Digital Library

[17]

Q. Mei, D. Cai, D. Zhang, and C. Zhai. Topic modeling with network regularization. In Proceedings of the 17th international conference on World Wide Web, WWW '08, pages 101--110, New York, NY, USA, 2008. ACM.

Digital Library

[18]

A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel. You are who you know: Inferring user profiles in online social networks. In WSDM, 2010.

Digital Library

[19]

N. Pathak, C. DeLong, A. Banerjee, and K. Erickson. Social topic models for community extraction. In The 2nd SNA-KDD Workshop, 2008.

[20]

A. Smola and S. Narayanamurthy. An architecture for parallel topic models. In VLDB, 2010.

Digital Library

[21]

M. Steyvers and T. L. Griffiths. Probabilistic topic models. Handbook of Latent Semantic Analysis, 2007.

[22]

M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths. Probabilistic author-topic models for information discovery. In SIGKDD, 2004.

Digital Library

[23]

V. Tuulos and H. Tirri. Combining topic models and social networks for chat data mining. In In Proc. of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, 2004.

Digital Library

[24]

X. Wang, N. Mohanty, and A. Mccallum. Group and topic discovery from relations and text. In In Proc. 3rd international workshop on Link discovery, pages 28--35. ACM, 2005.

Digital Library

[25]

M. J. Welch, U. Schonfeld, D. He, and J. Cho. Topical semantics of twitter links. In WSDM, 2011.

Digital Library

[26]

H. Zhang, B. Qiu, C. L. Giles, H. C. Foley, and J. Yen. An lda-based community structure discovery approach for large-scale social networks. In In IEEE International Conference on Intelligence and Security Informatics, pages 200--207, 2007.

[27]

D. Zhou, E. Manavoglu, J. Li, C. L. Giles, and H. Zha. Probabilistic models for discovering e-communities. In World Wide Web Conference, 2006.

Digital Library

Cited By

Schiavon L(2025)Addressing topic modelling via reduced latent space clusteringStatistical Methods & Applications10.1007/s10260-025-00779-zOnline publication date: 13-Feb-2025
https://doi.org/10.1007/s10260-025-00779-z
Hasan AKamal AKumar P(2025)k-InfNode: ranking top-k influential nodes in complex networks with random walkSoft Computing10.1007/s00500-025-10471-929:3(1677-1690)Online publication date: 15-Feb-2025
https://doi.org/10.1007/s00500-025-10471-9
Pacella MVasco PPapadia GGiliberti V(2024)An Assessment of Digitalization Techniques in Contact Centers and Their Impact on Agent Performance and Well-BeingSustainability10.3390/su1602071416:2(714)Online publication date: 14-Jan-2024
https://doi.org/10.3390/su16020714
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Index Terms

Social-network analysis using topic models

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Published In

cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 12 August 2012

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SIGIR '12

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SIGIR

SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Schiavon L(2025)Addressing topic modelling via reduced latent space clusteringStatistical Methods & Applications10.1007/s10260-025-00779-zOnline publication date: 13-Feb-2025
https://doi.org/10.1007/s10260-025-00779-z
Hasan AKamal AKumar P(2025)k-InfNode: ranking top-k influential nodes in complex networks with random walkSoft Computing10.1007/s00500-025-10471-929:3(1677-1690)Online publication date: 15-Feb-2025
https://doi.org/10.1007/s00500-025-10471-9
Pacella MVasco PPapadia GGiliberti V(2024)An Assessment of Digitalization Techniques in Contact Centers and Their Impact on Agent Performance and Well-BeingSustainability10.3390/su1602071416:2(714)Online publication date: 14-Jan-2024
https://doi.org/10.3390/su16020714
Lin LYu JHong FLai CChen SYuan X(2024)GraphInterpreter: a visual analytics approach for dynamic networks evolution exploration via topic modelsJournal of Visualization10.1007/s12650-024-00993-z27:5(909-924)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s12650-024-00993-z
Li SLiu TTan JZeng DGe S(2023)Trustable Co-Label Learning From Multiple Noisy AnnotatorsIEEE Transactions on Multimedia10.1109/TMM.2021.313775225(1045-1057)Online publication date: 2023
https://doi.org/10.1109/TMM.2021.3137752
Biagio MCipolla MMattina EMorreale V(2023)A Topic Clustering Method to Identify Online Threats against Soft Targets2023 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI62032.2023.00124(727-733)Online publication date: 13-Dec-2023
https://doi.org/10.1109/CSCI62032.2023.00124
Zhdankina MKolesnikova VRomanov SRudyuk D(2023)Comparative Analysis of Topics Covered by False and True News in the Context of the COVID-19 PandemicNetworks in the Global World VI10.1007/978-3-031-29408-2_2(21-35)Online publication date: 2-May-2023
https://doi.org/10.1007/978-3-031-29408-2_2
Patel KAdhikary RPatel ZBatra NGuttikunda S(2022)Samachar: Print News Media on Air Pollution in IndiaProceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3530190.3534812(401-413)Online publication date: 29-Jun-2022
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Yang EYao DLiu TDeng C(2022)Mutual Quantization for Cross-Modal Search with Noisy Labels2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00740(7541-7550)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.00740
Yan JLuo LXu CDeng CHuang H(2022)Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00013(31-40)Online publication date: Jun-2022
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