Article

Mining directed social network from message board

Authors:

Naohiro Matsumura,

David E. Goldberg,

Xavier LloràAuthors Info & Claims

WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web

Pages 1092 - 1093

https://doi.org/10.1145/1062745.1062884

Published: 10 May 2005 Publication History

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Abstract

In the paper, we present an approach to mining a directed social network from a message board on the Internet where vertices denote individuals and directed links denote the flow of influence. The influence is measured based on propagating terms among individuals via messages. The distance with respect to contextual similarity between individuals is acquired since the influence indicates the degree of their shared interest represented as terms.

References

[1]

N. Matsumura. Topic Diffusion in a Community. Chance Discovery, pages 84--97. Springer Verlag, 2003.

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[2]

N. Matsumura, D. E. Goldberg, and X. Xlorà. Mining social networks from message boards. IlliGAL Report No. 2005001, 2005.

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Aghababaei SMakrehchi M(2019)Interpolative self-training approach for link predictionIntelligent Data Analysis10.3233/IDA-18439023:6(1379-1395)Online publication date: 8-Nov-2019
https://doi.org/10.3233/IDA-184390
Zhang SDu ZWang JJiang H(2018)Discovering frequent induced subgraphs from directed networksIntelligent Data Analysis10.3233/IDA-17368122:6(1279-1296)Online publication date: 18-Dec-2018
https://doi.org/10.3233/IDA-173681
Al-Zeyadi MCoenen FLisitsa A(2017)On the mining and usage of Movement Patterns in large traffic networks2017 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2017.7881729(135-142)Online publication date: Feb-2017
https://doi.org/10.1109/BIGCOMP.2017.7881729
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Index Terms

Mining directed social network from message board
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks

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Published In

WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web

May 2005

454 pages

ISBN:1595930515

DOI:10.1145/1062745

Conference Chairs:
Allan Ellis
Southern Cross University, Australia
,
Tatsuya Hagino
Keio University, Japan
,
Program Chairs:
Fred Douglis
IBM Research
,
Prabhakar Raghavan
Verity, Inc.

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: 10 May 2005

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Aghababaei SMakrehchi M(2019)Interpolative self-training approach for link predictionIntelligent Data Analysis10.3233/IDA-18439023:6(1379-1395)Online publication date: 8-Nov-2019
https://doi.org/10.3233/IDA-184390
Zhang SDu ZWang JJiang H(2018)Discovering frequent induced subgraphs from directed networksIntelligent Data Analysis10.3233/IDA-17368122:6(1279-1296)Online publication date: 18-Dec-2018
https://doi.org/10.3233/IDA-173681
Al-Zeyadi MCoenen FLisitsa A(2017)On the mining and usage of Movement Patterns in large traffic networks2017 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2017.7881729(135-142)Online publication date: Feb-2017
https://doi.org/10.1109/BIGCOMP.2017.7881729
Al-Zeyadi MCoenen FLisitsa A(2016)Mining Frequent Movement Patterns in Large Networks: A Parallel Approach Using ShapesResearch and Development in Intelligent Systems XXXIII10.1007/978-3-319-47175-4_4(53-67)Online publication date: 5-Nov-2016
https://doi.org/10.1007/978-3-319-47175-4_4
Xia ZBu Z(2012)Community detection based on a semantic networkKnowledge-Based Systems10.1016/j.knosys.2011.06.01426(30-39)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.knosys.2011.06.014
Nygren E(2012)Simulation of User Participation and Interaction in Online Discussion GroupsModeling and Mining Ubiquitous Social Media10.1007/978-3-642-33684-3_8(138-157)Online publication date: 2012
https://doi.org/10.1007/978-3-642-33684-3_8
Nygren E(2011)Simulation of user participation and interaction in online discussion groupsProceedings of the 2011th International Conference on Modeling and Mining Ubiquitous Social Media - 2011 International Workshop on Modeling Social Media and 2011 International Workshop on Mining Ubiquitous and Social Environments10.5555/3120657.3120665(138-157)Online publication date: 9-Oct-2011
https://dl.acm.org/doi/10.5555/3120657.3120665
Makrehchi MMobasher BBurke RJannach DAdomavicius G(2011)Social link recommendation by learning hidden topicsProceedings of the fifth ACM conference on Recommender systems10.1145/2043932.2043968(189-196)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1145/2043932.2043968
Stanley Laine MErcal GLuo B(2011)User Groups in Social NetworksProceedings of the 2011 44th Hawaii International Conference on System Sciences10.1109/HICSS.2011.472(1-10)Online publication date: 4-Jan-2011
https://dl.acm.org/doi/10.1109/HICSS.2011.472
Kaltenbrunner AGonzalez GRuiz De Querol RVolkovich Y(2011)Comparative analysis of articulated and behavioural social networks in a social news sharing websiteNew Review of Hypermedia and Multimedia10.1080/13614568.2011.59819217:3(243-266)Online publication date: Dec-2011
https://doi.org/10.1080/13614568.2011.598192
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