The predictive value of young and old links in a social network

Hung Hsuan Chen, David J. Miller, C. Lee Giles

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

5 Scopus citations

Abstract

Recent studies show that vertex similarity measures are good at predicting link formation over the near term, but are less effective in predicting over the long term. This indicates that, generally, as links age, their degree of influence diminishes. However, few papers have systematically studied this phenomenon. In this paper, we apply a supervised learning approach to study age as a factor for link formation. Experiments on several real-world datasets show that younger links are more informative than older ones in predicting the formation of new links. Since older links become less useful, it might be appropriate to remove them when studying network evolution. Several previously observed network properties and network evolution phenomena, such as "the number of edges grows super-linearly in the number of nodes" and "the diameter is decreasing as the network grows", may need to be reconsidered under a dynamic network model where old, inactive links are removed.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013
PublisherAssociation for Computing Machinery
Pages43-48
Number of pages6
ISBN (Print)9781450321914
DOIs
StatePublished - 2013
Event3rd ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013 - New York, NY, United States
Duration: Jun 22 2013Jun 27 2013

Publication series

NameProceedings of the ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013

Other

Other3rd ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013
CountryUnited States
CityNew York, NY
Period6/22/136/27/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

Fingerprint Dive into the research topics of 'The predictive value of young and old links in a social network'. Together they form a unique fingerprint.

  • Cite this

    Chen, H. H., Miller, D. J., & Giles, C. L. (2013). The predictive value of young and old links in a social network. In Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013 (pp. 43-48). (Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2013). Association for Computing Machinery. https://doi.org/10.1145/2484702.2484711