Temporal link prediction based on node dynamics

Abstract

Temporal link prediction (TLP) aims to predict future links and is attracting increasing attention. The diverse interaction patterns and nonlinear nature of temporal networks make it challenging to design high-accuracy general prediction algorithms. Black-box models such as network embeddings and graph neural networks have gradually become the mainstream for TLP, mainly due to their high prediction accuracy. However, a good TLP algorithm also needs to assist us in exploring the network evolution mechanism. Accuracy-oriented black-box methods cannot sufficiently explain the evolution mechanism because of their low interpretability. Hence there is a need for a high-accuracy white-box TLP method. In this paper, we turn the perspective of link prediction to node itself, a more microscopic level whose dynamic nature we take to predict future links. Two dynamic properties – node activity and node loyalty – are extracted and quantified. Activity is the basic ability of a node to obtain links, and loyalty is its ability to maintain its current link state. Based on the above two properties, we propose a Develop-Maintain Activity Backbone (DMAB) model as our TLP algorithm. Comparative experiments with six state-of-the-art black-box methods on 12 real networks illustrate that DMAB has excellent prediction performance and well captures network evolution mechanisms.