DSMN: A New Approach for Link Prediction in Multilplex Networks.

In a multiplex network, there exists different types of relationships between the same set of nodes such as people which have different accounts in online social networks. Previous researches have proved that in a multiplex network the structural features of different layers are interrelated. Therefore, effective use of information from other layers can improve link prediction accuracy in a specific layer. In this paper, we propose a new inter-layer similarity metric DSMN, for predicting missing links in multiplex networks. We then combine this metric with a strong intralayer similarity metric to enhance the performance of link prediction. The efficiency of our proposed method has been evaluated on both real-world and synthetic networks and the experimental results indicate the outperformance of the proposed method in terms of prediction accuracy in comparison with similar methods. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Information & Communication Technology Research (2251-6107) is the property of Iran Telecom Research Center (ITRC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)