New prediction method for data spreading in social networks based on machine learning algorithm

Abstract

Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices by active vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of the next article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBLP and Pubmed datasets, respectively.