Link prediction in multiplex online social networks

Jalili, M, Orouskhani, Y, Asgari, M, Alipourfard, N and Perc, M 2017, 'Link prediction in multiplex online social networks', Royal Society Open Science, vol. 4, no. 2, pp. 1-11.

Document type: Journal Article
Collection: Journal Articles

Title Link prediction in multiplex online social networks
Author(s) Jalili, M
Orouskhani, Y
Asgari, M
Alipourfard, N
Perc, M
Year 2017
Journal name Royal Society Open Science
Volume number 4
Issue number 2
Start page 1
End page 11
Total pages 11
Publisher The Royal Society Publishing
Abstract Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Subject Dynamical Systems in Applications
Information Retrieval and Web Search
Control Systems, Robotics and Automation
DOI - identifier 10.1098/rsos.160863
Copyright notice © 2017 The Authors
ISSN 2054-5703
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Citation counts: TR Web of Science Citation Count  Cited 34 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
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