You are here

Link Prediction with Social Vector Clocks

Authors: 

Conrad Lee, Bobo Nick, Ulrik Brandes, Pádraig Cunningham

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.
Conference Name: 
19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings: 
19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 784-792).
Digital Object Identifer (DOI): 
10.na
Publication Date: 
11/08/2013
Conference Location: 
United States of America
Research Group: 
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No
Publication document: