Article

Group and topic discovery from relations and text

Authors:

Natasha Mohanty,

Andrew McCallumAuthors Info & Claims

LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery

Pages 28 - 35

https://doi.org/10.1145/1134271.1134276

Published: 21 August 2005 Publication History

Abstract

We present a probabilistic generative model of entity relationships and textual attributes that simultaneously discovers groups among the entities and topics among the corresponding text. Block-models of relationship data have been studied in social network analysis for some time. Here we simultaneously cluster in several modalities at once, incorporating the words associated with certain relationships. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or Blockstructures for votes, the Group-Topic model's joint inference improves both the groups and topics discovered.

References

[1]

D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. In SIGKDD, 2000.

Digital Library

[2]

R. Bekkerman, R. E. Yaniv, and A. McCallum. Multi-way distributional clustering via pairwise interactions. In ICML, 2005.

Digital Library

[3]

I. Bhattacharya and L. Getoor. Deduplication and group detection using links. In LinkKDD, 2004.

[4]

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. JMLR, 3:993--1022, 2003.

Digital Library

[5]

K. Carley. A theory of group stability. American Sociological Review, 56(3):331--354, 1991.

[6]

K. Carley. A comparison of artificial and human organizations. Journal of Economic Behavior and Organization, 56:175--191, 1996.

[7]

G. Cox and K. Poole. On measuring the partisanship in roll-call voting: The U.S. House of Represenatatives, 1887--1999. American Journal of Political Science, 46(1):477--489, 2002.

[8]

W. W. Denham, C. K. McDaniel, and J. R. Atkins. Aranda and Alyawarra kinship: A quantitative argument for a double helix model. American Ethnologist, 6(1):1--24, 1979.

[9]

I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In SIGKDD, 2003.

Digital Library

[10]

D. Fenn, O. Suleman, J. Efstathiou, and N. Johnson. How does Europe make its mind up? Connections, cliques, and compatibility between countries in the Eurovision song contest. arXiv:physics/0505071, 2005.

[11]

S. Hix, A. Noury, and G. Roland. Power to the parties: Cohesion and competition in the European Parliament, 1979--2001. British Journal of Political Science, 35(2):209--234, 2005.

[12]

A. Jakulin and W. Buntine. Analyzing the US Senate in 2003: Similarities, networks, clusters and blocs, 2004.

[13]

C. Kemp, T. L. Griffiths, and J. Tenenbaum. Discovering latent classes in relational data. Technical report, MIT CSAIL, 2004.

[14]

D. Krackhardt and K. M. Carley. A PCANS model of structure in organization. In Int. Sym. on Command and Control Research and Technology, June 1998.

[15]

J. Kubica, A. Moore, J. Schneider, and Y. Yang. Stochastic link and group detection. In AAAI, 2002.

Digital Library

[16]

A. McCallum, A. Corrada-Emanuel, and X. Wang. Topic and role discovery in social networks. In IJCAI, 2005.

Digital Library

[17]

K. Nowicki and T. A. Snijders. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 2001.

[18]

A. Pajala, A. Jakulin, and W. Buntine. Parliamentary group and individual voting behavior in Finnish Parliamentin year 2003: A group cohesion and voting similarity analysis, 2004.

[19]

M. Sparrow. The application of network analysis to criminal intelligence: an assessment of prospects. Social Networks, 13:251--274, 1991.

[20]

E. Voeten. Documenting votes in the UN General Assembly. http://home.gwu.edu/~voeten/UNVoting.htm_Toc82404232.

[21]

S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.

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Levy DMayer TRaviv A(2022)Economists in the 2008 financial crisis: Slow to see, fast to actJournal of Financial Stability10.1016/j.jfs.2022.10098660(100986)Online publication date: Jun-2022
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Llano JCeballos HCantú F(2021)How Is Twitter Talking About COVID-19?Advances in Data Science and Information Engineering10.1007/978-3-030-71704-9_7(111-121)Online publication date: 30-Oct-2021
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Index Terms

Group and topic discovery from relations and text
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

cover image ACM Other conferences

LinkKDD '05: Proceedings of the 3rd international workshop on Link discovery

August 2005

101 pages

ISBN:1595932151

DOI:10.1145/1134271

Program Chairs:
Jafar Adibi
University of Southern California, Marina del Rey, CA
,
Marko Grobelnik
J.Stefan Institute, Ljubljana, Slovenia
,
Dunja Mladenic
J.Stefan Institute, Ljubljana, Slovenia.
,
Patrick Pantel
University of Southern California, Marina del Rey, CA

Copyright © 2005 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

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KDD05

KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 25, 2005

Illinois, Chicago

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Cited By

Fu SWang LYang J(2024)A review on network representation learning with multi-granularity perspectiveIntelligent Data Analysis10.3233/IDA-22732828:1(3-32)Online publication date: 3-Feb-2024
https://doi.org/10.3233/IDA-227328
Levy DMayer TRaviv A(2022)Economists in the 2008 financial crisis: Slow to see, fast to actJournal of Financial Stability10.1016/j.jfs.2022.10098660(100986)Online publication date: Jun-2022
https://doi.org/10.1016/j.jfs.2022.100986
Llano JCeballos HCantú F(2021)How Is Twitter Talking About COVID-19?Advances in Data Science and Information Engineering10.1007/978-3-030-71704-9_7(111-121)Online publication date: 30-Oct-2021
https://doi.org/10.1007/978-3-030-71704-9_7
Feng JRao YXie HWang FLi Q(2019)User group based emotion detection and topic discovery over short textWorld Wide Web10.1007/s11280-019-00760-3Online publication date: 12-Dec-2019
https://doi.org/10.1007/s11280-019-00760-3
Liu TXue FSun JSun X(2019)A survey of event analysis and mining from social multimediaMultimedia Tools and Applications10.1007/s11042-019-7567-7Online publication date: 24-Apr-2019
https://doi.org/10.1007/s11042-019-7567-7
Alduaiji NDatta A(2018)An Empirical Study on Sentiments in Twitter Communities2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00167(1166-1172)Online publication date: Nov-2018
https://doi.org/10.1109/ICDMW.2018.00167
Yang TJin RChi YZhu S(2018)Combining Link and Content for Community DetectionEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_214(301-312)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_214
Zhang HXu SWang ZZhu LLuo HDeng DWu H(2017)Text and Data Mining of Social Media in Science and Technology Publicity2017 Portland International Conference on Management of Engineering and Technology (PICMET)10.23919/PICMET.2017.8125345(1-7)Online publication date: Jul-2017
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王全(2017)Relation Topic Model Based on LinksComputer Science and Application10.12677/CSA.2017.7302907:03(232-239)Online publication date: 2017
https://doi.org/10.12677/CSA.2017.73029
Chen HYin HLi XWang MChen WChen TBarrett RCummings RAgichtein EGabrilovich E(2017)People Opinion Topic ModelProceedings of the 26th International Conference on World Wide Web Companion10.1145/3041021.3051159(1353-1359)Online publication date: 3-Apr-2017
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