Link Prediction Based on Gravitational Field of Complex Network

Abstract

At present, most link prediction algorithms only consider the connection between nodes and nodes, which is one-to-one, and it does not consider the contribution to the neighbor nodes of the nodes to the prediction performance of the target nodes, having poor prediction performance on sparse networks. To solve this problem, a link prediction algorithm based on the gravitational field of complex network is proposed in this paper. Firstly, the nodes in the complex network are abstracted into the mass points in the Gravitation field, and the complex network gravitational field model is established from the point of view of physics. Secondly, the direct gravitational value between two nodes is calculated by using the complex network gravitational field model, and then the indirect gravitational values of the first order neighbors of the target nodes between two nodes are calculated. After that, summed the direct gravitational values with the indirect gravitational values as the similarity value between the two nodes. Finally, the gravitational similarity matrix is constructed and the gravitational similarity matrix is normalized. In this paper, a large number of experiments are carried out on four real datasets. The Experiments results show that, compared with the traditional link prediction algorithms, the prediction performance of most traditional link prediction algorithms has been improved to a certain extent by using the presented method to optimize various traditional link prediction algorithms, and the validity and feasibility of link prediction algorithm based on the gravitational field of complex network are affirmed.