Capturing missing edges in social networks using vertex similarity

Authors:
Hung-Hsuan Chen;Liang Gou;Xiaolong Zhang;Clyde Lee Giles
Affiliations:
Pennsylvania State University, State College, PA, USA;Pennsylvania State University, State College, PA, USA;Pennsylvania State University, State College, PA, USA;Pennsylvania State University, State College, PA, USA
Venue:
Proceedings of the sixth international conference on Knowledge capture
Year:
2011

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Abstract

We introduce the graph vertex similarity measure, Relation Strength Similarity (RSS), that utilizes a network's topology to discover and capture similar vertices. The RSS has the advantage that it is asymmetric; can be used in a weighted network; and has an adjustable "discovery range" parameter that enables exploration of friend of friend connections in a social network. To evaluate RSS we perform experiments on a coauthorship network from the CiteSeerX database. Our method significantly outperforms other vertex similarity measures in terms of the ability to predict future coauthoring behavior among authors in the CiteSeerX database for the near future 0 to 4 years out and reasonably so for 4 to 6 years out.