Link prediction in social networks with edge aging

Ricky Laishram, Kishan Mehrotra, Chilukuri K Mohan

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

4 Scopus citations

Abstract

In social networks that change with time, an important problem is the prediction of new links that may be formed in the future. Existing works on link prediction have focused only on networks where links are permanent, an assumption that is not valid in many real world social networks. In many real world networks, in addition to new links being created, existing links also get removed. In this paper, we extend existing link prediction methods and apply a supervised learning algorithm to networks with non-permanent links. The results we obtain on Twitter @-mention networks show that our method performs very well in such networks.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages606-613
Number of pages8
ISBN (Electronic)9781509044597
DOIs
StatePublished - Jan 11 2017
Event28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016 - San Jose, United States
Duration: Nov 6 2016Nov 8 2016

Other

Other28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016
CountryUnited States
CitySan Jose
Period11/6/1611/8/16

Keywords

  • Artificial intelligence
  • Link prediction
  • Machine learning
  • Social network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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