Link prediction in complex networks based on communication capacity and local paths

Link prediction is one of the most essential and significant research issues in network science, which aims at finding unknown, missing or future links in complex networks. Numerous similarity-based algorithms have been widely applied due to low computational cost and high prediction accuracy. To achieve a good balance between prediction accuracy and computational complexity, we design a novel link prediction algorithm to predict potential links based on Communication Capabilities and Local Paths (CCLP). The core idea of the proposed algorithm is that the similarity of node pairs is closely bound up with the amount of resources transmitted between them. In this algorithm, we introduce communicability network matrix to calculate the resource transmission capability of nodes, and then integrate it with local paths to measure the amount of resources transferred between nodes. We conduct several groups of comparative experiments on 12 real-world networks to validate the effectiveness of the proposed algorithm. Experimental results demonstrate that CCLP has achieved better performance than four classical similarity indices and five popular algorithms. Graphic abstract