Link prediction across multiple social networks

Muhammad Aurangzeb Ahmad, Zoheb Borbora, Jaideep Srivastava, Noshir Contractor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

The problem of link prediction has been studied extensively in literature. There are various versions of the link prediction problem e.g., link existence problem, link removal problem, predicting edge weights over time etc. In this paper we describe a new type of link prediction problem called the Inter-network link-prediction problem where the task is to predict links across different networks. Thus given a set of nodes which participate in multiple networks the task is to determine if one can predict the edges that occur in one network by only using node attribute and edge information from other networks. We use insights from theories of evolution of social communication networks and the MTML framework to derive models which can be used to make link predictions across networks. For the experiments data from different types of social networks from a Massively Multiplayer Online Role Playing Game (MMORPG) is used.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages911-918
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2010
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
CountryAustralia
CitySydney, NSW
Period14/12/1017/12/10

    Fingerprint

Keywords

  • Inter-network link prediction
  • Link prediction
  • Multiple social networks
  • Social networks

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ahmad, M. A., Borbora, Z., Srivastava, J., & Contractor, N. (2010). Link prediction across multiple social networks. In Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 (pp. 911-918). [5693393] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2010.79