Abstract
Research on social networks is at its peak in the current era of big data, especially in the field of computer research. Link prediction in social networks has attracted an increasing number of researchers. However, most of the current studies have focused on the prediction of the visible relationships between users, ignoring the existence of invisible relationships. The same as visible relationships, invisible relationships are also an indispensable part of social networks, and they can uncover more potential relationships between users. To better understand invisible relationship, definition, types, and characteristics of invisible relationship have been introduced in this paper. Also an influence algorithm is proposed to speculate on the existence of invisible edges between users. The algorithm is based on three indicators, namely, the occasional contact degree, interest coincidence degree, and the popularity of users, and it takes the influence as reference. By comparing with the threshold, Θ , defined in advance, users with relationships stronger than Θ are viewed as possessing invisible relationships. The feasibility and accuracy of the algorithm are proven by extensive numerical experiments compared with one well-known and widely used method, i.e., the common neighbors (CN).
Highlights
The progress of science and technology makes communication more convenient, especially the development of instant messaging software and mobile networks
(2) To predict the invisible relationships between users, we propose an influence algorithm based on three indicators, i.e., occasional contact degree, interest coincidence degree, and popularity of users
It is a comprehensive index used to measure the invisible relationship between users, and it is the compromise of the occasional contact degree, interest coincidence degree, and user popularity, which is presented as formula (9) as follows: Influenceðu, vÞ
Summary
The progress of science and technology makes communication more convenient, especially the development of instant messaging software and mobile networks. By analyzing the data in social networks, much information of users can be gained which can help to provide better personal services for users, e.g., recommendation of web pages or goods or prediction of new links [2]. Among these applications, the problem of prediction potential links has attracted more and more attentions in recent years [3,4,5]. Prediction of invisible relationship is an important complement of link prediction in social networks, which can help to enhance the security of social networks (2) To predict the invisible relationships between users, we propose an influence algorithm based on three indicators, i.e., occasional contact degree, interest coincidence degree, and popularity of users. The experimental results are reported and analyzed in “Design and Analysis of Experiment.” “Conclusion” concludes the proposed invisible relationship and prediction algorithm and discusses the future work
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