By Topic

Scalable Link Prediction in Social Networks Based on Local Graph Characteristics

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Papadimitriou, A. ; Dept. of Inf., Aristotle Univ., Thessaloniki, Greece ; Symeonidis, P. ; Manolopoulos, Y.

Online social networks (OSNs) like Face book, My space, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the "algorithmic small world hypothesis." As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our Friend Link algorithm outperforms other approaches in terms of effectiveness and efficiency in all data sets. Finally, we discuss extensively various experimental considerations, such as a possible Map Reduce implementation of Friend Link algorithm to achieve scalability.

Published in:

Information Technology: New Generations (ITNG), 2012 Ninth International Conference on

Date of Conference:

16-18 April 2012