A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Abstract

Online 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 paper, 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.

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

“…Human behavior detection is used in many applications such as video surveillance, motion detection and human‐computer interaction 11 . In addition, hierarchical approaches as multilayered methods can calculate the descriptive pattern of an activity through training sequences 12‐14 . Also, these methods are suitable for detection of actions and gestures (eg, small movements and gestures) such as relatively simple human sequential movements (eg, walking, jumping and shaking hands) 13,15 …”