2017
DOI: 10.1098/rsos.160863
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Link prediction in multiplex online social networks

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 multiple… Show more

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Cited by 164 publications
(86 citation statements)
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“…To quantify the accuracy of the proposed prediction method, we used different evaluation metrics including mean average error (MAE), root mean square error (RMSE), area under receiver operating characteristic curve (AUC), and area under precision‐recall curve (AUPR). MAE is the average absolute difference between the real values and the predicted values (Jalili, Orouskhani, Asgari, Alipourfard, & Perc, ). RMSE is the standard deviation of the differences between the actual values and the predicted ones (Jalili et al, ).…”
Section: Methodsmentioning
confidence: 99%
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“…To quantify the accuracy of the proposed prediction method, we used different evaluation metrics including mean average error (MAE), root mean square error (RMSE), area under receiver operating characteristic curve (AUC), and area under precision‐recall curve (AUPR). MAE is the average absolute difference between the real values and the predicted values (Jalili, Orouskhani, Asgari, Alipourfard, & Perc, ). RMSE is the standard deviation of the differences between the actual values and the predicted ones (Jalili et al, ).…”
Section: Methodsmentioning
confidence: 99%
“…and area under precision-recall curve (AUPR). MAE is the average absolute difference between the real values and the predicted values (Jalili, Orouskhani, Asgari, Alipourfard, & Perc, 2017). RMSE is the standard deviation of the differences between the actual values and the predicted ones (Jalili et al, 2017).…”
Section: Evaluation Metricsmentioning
confidence: 99%
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“…For example, for ER-ER multiplex networks with the lowest correlation ω=−0.1, δ c =0.27, while for single ER networks, δ c =0. 26. That is to say, minorities in the multiplex networks need more initial support to form the clusters at the steady state than minorities in single networks [51].…”
Section: Multi-scale Abp Model On Multiplex Network With Different Dmentioning
confidence: 98%
“…multiplex networks (e.g. diffusion processes [22][23][24], epidemic spreading [25][26][27][28][29][30][31][32][33], percolation [34][35][36][37], and evolutionary game [38][39][40][41][42]).…”
Section: Introductionmentioning
confidence: 99%