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.

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Index Terms

Social-network analysis using topic models

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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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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
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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
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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
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Costa GOrtale R(2022)Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clustersInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.05.002147(23-39)Online publication date: Aug-2022
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