Discovering missing links in networks using vertex similarity measures

Hung Hsuan Chen, Liang Gou, Xiaolong Zhang, C. Lee Giles

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

48 Scopus citations

Abstract

Vertex similarity measure is a useful tool to discover the hidden relationships of vertices in a complex network. We introduce relation strength similarity (RSS), a vertex similarity measure that could better capture potential relationships of real world network structure. RSS is unique in that is is an asymmetric measure which could be used for a more general purpose social network analysis; allows users to explicitly specify the relation strength between neighboring vertices for initialization; and offers a discovery range parameter could be adjusted by users for extended network degree search. To show the potential of vertex similarity measures and the superiority of RSS over other measures, we conduct experiments on two real networks, a biological network and a coauthorship network. Experimental results show that RSS is better in discovering the hidden relationships of the networks.

Original languageEnglish (US)
Title of host publication27th Annual ACM Symposium on Applied Computing, SAC 2012
Pages138-143
Number of pages6
DOIs
StatePublished - Jul 12 2012
Event27th Annual ACM Symposium on Applied Computing, SAC 2012 - Trento, Italy
Duration: Mar 26 2012Mar 30 2012

Other

Other27th Annual ACM Symposium on Applied Computing, SAC 2012
CountryItaly
CityTrento
Period3/26/123/30/12

All Science Journal Classification (ASJC) codes

  • Software

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