Link prediction in multiplex online social networks
| Autor: | Matjaž Perc, Yasin Orouskhani, Nazanin Alipourfard, Milad Asgari, Mahdi Jalili |
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| Rok vydání: | 2017 |
| Předmět: |
social networks
Service (systems architecture) Dynamic network analysis Computer science Microblogging 02 engineering and technology Machine learning computer.software_genre Naive Bayes classifier Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Social media lcsh:Science link prediction Multidisciplinary Social network business.industry signed networks complex networks Complex network Support vector machine machine learning Evolving networks lcsh:Q 020201 artificial intelligence & image processing Artificial intelligence business computer Research Article |
| Zdroj: | Royal Society Open Science, Vol 4, Iss 2 (2017) Royal Society Open Science |
| ISSN: | 2054-5703 |
| DOI: | 10.1098/rsos.160863 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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