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Structural-interaction link prediction in microblogs

Published: 13 May 2013 Publication History

Abstract

Link prediction in Microblogs by using unsupervised methods aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. In this work, we define the retweet similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments on the real world Twitter data show our model outperforms state-of-the-art methods.

References

[1]
D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In Proc. NIPS, pages 556--562. ACM, December 2001.
[2]
D. Yin, L. Hong, and B. D. Davison. Structural link analysis and prediction in microblogs. In Proc. CIKM, pages 1163--1168. ACM, October 2011.

Cited By

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  • (2017)Link Inference in Dynamic Heterogeneous Information Network: A Knapsack-Based ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2017.27150694:3(80-92)Online publication date: Sep-2017
  • (2016)Location PredictionACM Transactions on Intelligent Systems and Technology10.1145/28168247:3(1-25)Online publication date: 11-Feb-2016
  • (2016)A supervised learning approach to link prediction in TwitterSocial Network Analysis and Mining10.1007/s13278-016-0333-16:1Online publication date: 2-May-2016
  • Show More Cited By

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Published In

cover image ACM Other conferences
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
May 2013
1636 pages
ISBN:9781450320382
DOI:10.1145/2487788
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
  • CGIBR: Comite Gestor da Internet no Brazil

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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Author Tags

  1. link prediction
  2. microblogs
  3. structure-interaction

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  • Poster

Conference

WWW '13
Sponsor:
  • NICBR
  • CGIBR
WWW '13: 22nd International World Wide Web Conference
May 13 - 17, 2013
Rio de Janeiro, Brazil

Acceptance Rates

WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2017)Link Inference in Dynamic Heterogeneous Information Network: A Knapsack-Based ApproachIEEE Transactions on Computational Social Systems10.1109/TCSS.2017.27150694:3(80-92)Online publication date: Sep-2017
  • (2016)Location PredictionACM Transactions on Intelligent Systems and Technology10.1145/28168247:3(1-25)Online publication date: 11-Feb-2016
  • (2016)A supervised learning approach to link prediction in TwitterSocial Network Analysis and Mining10.1007/s13278-016-0333-16:1Online publication date: 2-May-2016
  • (2014)Populating knowledge base with collective entity mentionsProceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3191835.3191955(604-611)Online publication date: 17-Aug-2014
  • (2014)TSBMProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0210.1109/WI-IAT.2014.98(194-201)Online publication date: 11-Aug-2014

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