An Algorithm Based on Influence to Predict Invisible Relationship

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, $\Theta$ , defined in advance, users with relationships stronger than $\Theta$ 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).