CLP-ID: Community-based link prediction using information diffusion

Abstract In recent years, most link prediction algorithms have focused on node similarity owing to the associated low computational complexity and promising accuracy. In addition to the classical CN-based indexes, some methods are based on network features, such as community structure, information dissemination, and intermediary influence probability, which are used for link prediction. Although these methods provide new insight into the problem and achieve improvements in certain respects, they also have some limitations. For example, it is difficult to predict target links if the number of interconnections between communities is small. However, most studies aim at achieving higher link prediction accuracy even though a network obtained by these methods is not optimized for information spread. Therefore, we propose a community-based link prediction method using an information diffusion algorithm (CLP-ID) to predict the missing links. First, we present a community detection algorithm that divides the network into clusters. Then, a novel algorithm based on information diffusion and community structure is proposed to predict target links. Finally, we conduct experiments on real-world networks to validate the performance of the proposed algorithm and compare it with state-of-the-art algorithms. Statistical tests demonstrate that the proposed method significantly differs from state-of-the-art algorithms.