Abstract
Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.
Highlights
The analysis of social networks has drawn increasing attention in the field of sociology
To solve the problem of temporal links prediction, this paper proposes a dynamic similarity framework with an improved gravity model to estimate the future links in temporal networks
Suppose given a temporal network G T with T separated slices, where each slice is modeled as a mono-layer network, T is the total number of layers, and t = 1, 2, ..., T; the model is denoted by
Summary
The analysis of social networks has drawn increasing attention in the field of sociology. It analyzes and explores the potential relations between social objects [1]. The rapid development of social media has brought us plentiful data sources, along with enormous challenges such as data incompletion and dynamic changes [2]. The dynamic changes may lead the nodes and links to appear and disappear in the future, which makes the underlying graph longitudinal [3]. The former research, known as missing links prediction, has been fruitful during the last decade, whereas the prediction of future links is more challenging to estimate the upcoming connections with limited social information. A recommendation system [7], as a typical application of temporal links prediction, is designed for individuals to make friends or purchase goods via efficient predicted results, which brings considerable benefits for corporations
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