Distributed Temporal Link Prediction Algorithm Based on Label Propagation

Link prediction has steadily become an important research topic in the area of complex networks. However, the current link prediction algorithms typi cally neglect the network evolution and tend to exhibit low accuracy and scal ability when applied to large-scale organisations. In this article, we propose a novel distributed temporal link prediction algorithm based on label propagation (DTLPLP), governed by the dynamical properties of the interactions between nodes. In particular, nodes are associated with labels, which include details of their sources and the corresponding similarity value. When such labels are propagated across neighbouring nodes, they are updated based on the weights of the incident links, and the values from same source nodes are aggregated to evaluate the scores of links in the predicted network. Furthermore, DTLPLP has been designed to be distributed and parallelised, and thus is suitable for large-scale network analysis. As part of the validation process, we have de signed a prototype system developed in Pregel, which is a distributed network analysis framework. Experiments are conducted on the Enron e-mail network and the General Relativity and Quantum Cosmology Scientific Collaboration network. The experimental results show that when compared to the most of link prediction algorithms, DTLPLP offers enhanced accuracy, stability and scalability.