Abstract
AbstractOnline social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi‐layered so that there may be multiple links between a user' on different social networks. In this article, the link prediction problem for the same user in a two‐layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two‐layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single‐layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.
Highlights
Many real-world systems can be described as networks that have nodes with the role of objects [1]
The proposed similarity measure uses only the topographic information of the network, so its results should be compared with other classical similarity measures in the link prediction problem
Using similarity measure to predict the probability of future interactions is one of the common methods in link prediction problem
Summary
Many real-world systems can be described as networks that have nodes with the role of objects [1]. In [11], a new similarity measure is proposed for the link prediction based on local structures in social networks. This measure is calculated through a supervised learning model with an observer based on estimating the similarity of source and destination nodes on a large database. Researchers have proposed various methods to find missing links [7,8,9] Most of these methods are calculated based on a similarity measure on neighboring nodes [11]. An efficient solution to the problem of link prediction in multi-layer social networks is presented, where a novel similarity measure is used to calculate similarity.
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