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Title:Link prediction in multiplex online social networks
Authors:ID Jalili, Mahdi (Author)
ID Orouskhani, Yasin (Author)
ID Asgari, Milad (Author)
ID Alipourfard, Nazanin (Author)
ID Perc, Matjaž (Author)
Files:.pdf Royal_Society_Open_Science_2017_Jalili_et_al._Link_prediction_in_multiplex_online_social_networks.pdf (940,17 KB)
MD5: 67C67B181B9FBD9EBBAE9A33F3FC041F
URL http://rsos.royalsocietypublishing.org/lookup/doi/10.1098/rsos.160863
Work type:Scientific work (r2)
Typology:1.01 - Original Scientific Article
Organization:FNM - Faculty of Natural Sciences and Mathematics
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%.
Keywords:social networks, complex networks, signed networks, link prediction, machine learning
Year of publishing:2017
Publication status in journal:Published
Article version:Publisher's version of article
Number of pages:str. 1-11
Numbering:št. 2, Letn. 4
ISSN on article:Y507-6544
COBISS.SI-ID:22983432 New window
DOI:10.1098/rsos.160863 New window
Publication date in DKUM:08.08.2017
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Record is a part of a journal

Title:Royal Society Open Science
Publisher:#The #Royal Society
COBISS.SI-ID:3219791 New window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project number:P5-0027
Name:Prilagajanje slovenskega gospodarstva in razvojna identiteta Slovenije v EU


License:CC BY 4.0, Creative Commons Attribution 4.0 International
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:08.08.2017

Secondary language

Keywords:socialna omrežja, družbena omrežja, kompleksna omrežja, podpisana omrežja, napovedovanje povezav, strojno učenje


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