Link prediction in multiplex online social networks

Mahdi Jalili (Author), Yasin Orouskhani (Author), Milad Asgari (Author), Nazanin Alipourfard (Author), Matjaž Perc (Author)

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

družbena omrežja;kompleksna omrežja;podpisana omrežja;napovedovanje povezav;strojno učenje;ne zaključna dela;social networks;complex networks;signed networks;link prediction;machine learning;

Data

Language:	English
Year of publishing:	2017
Typology:	1.01 - Original Scientific Article
Organization:	UM FNM - Faculty of Natural Sciences and Mathematics
UDC:	53
COBISS:	22983432
ISSN:	Y507-6544
Views:	1376
Downloads:	444
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	Analiza omrežij (komuniciranje);Družbena omrežja (internet);Napovedovanje;Hiperpovezave;Strojno učenje;
URN:	URN:SI:UM:
Type (COBISS):	Article
Pages:	str. 1-11
Volume:	ǂVol. ǂ4
Issue:	ǂiss. ǂ2
Chronology:	2017
DOI:	10.1098/rsos.160863
ID:	10852811