| Naslov: | Link prediction in multiplex online social networks |
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| Avtorji: | ID Jalili, Mahdi (Avtor)
ID Orouskhani, Yasin (Avtor)
ID Asgari, Milad (Avtor)
ID Alipourfard, Nazanin (Avtor)
ID Perc, Matjaž (Avtor) |
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| Datoteke: |  Royal_Society_Open_Science_2017_Jalili_et_al._Link_prediction_in_multiplex_online_social_networks.pdf (940,17 KB)
MD5: 67C67B181B9FBD9EBBAE9A33F3FC041F
PID: 20.500.12556/dkum/d68e30e1-40bc-40f7-9b1d-4cc941616c6c
 http://rsos.royalsocietypublishing.org/lookup/doi/10.1098/rsos.160863
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FNM - Fakulteta za naravoslovje in matematiko
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| Opis: | 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%. |
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| Ključne besede: | social networks, complex networks, signed networks, link prediction, machine learning |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Leto izida: | 2017 |
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| Št. strani: | str. 1-11 |
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| Številčenje: | Letn. 4, št. 2 |
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| PID: | 20.500.12556/DKUM-67238  |
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| ISSN: | Y507-6544 |
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| UDK: | 53 |
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| COBISS.SI-ID: | 22983432 |
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| DOI: | 10.1098/rsos.160863 |
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| ISSN pri članku: | Y507-6544 |
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| NUK URN: | URN:SI:UM:DK:0WOMC3GN |
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| Datum objave v DKUM: | 08.08.2017 |
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| Število ogledov: | 1788 |
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| Število prenosov: | 521 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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