GFNC: Unsupervised Link Prediction Based on Gravitational Field and Node Contraction

Currently, most existing link prediction algorithms simply study the interrelationships between node pairs without considering the interaction force and the higher order relationships between node pairs. In order to find a solution to this problem, the concept of the gravitational field is introduced in this article, and then, a novel algorithmic framework is proposed from the perspective of physics. The framework is applied to the classic link prediction algorithms to effectively enhance their prediction performance. First, the node contraction method is applied to measure the node importance, and a similarity-based link prediction algorithm is used to calculate the similarity values between node pairs. Second, the importance of nodes is introduced into the gravitational field model as the mass attribute, and the similarity values between node pairs are used as a distance metric between node pairs. Thereby, a gravitational field model of the complex network from the perspective of physics is established. Finally, the edges of the undirected complex network are assigned the weights, and a weighted local random walking-based link prediction algorithm is proposed. The link prediction method is adopted to evaluate the reasonableness and practical value of the gravitational field model. Experimental results show that most link prediction algorithms using the proposed algorithmic framework have got improvement with a minimum improvement of 2% and a maximum improvement of 33%; thus, the effectiveness and feasibility of the algorithm are verified.