Link prediction in directed complex networks: combining similarity-popularity and path patterns mining

Discovering new relationships between entities in networked data is essential in various applications such as sociology, security, physics, and biology. This paper introduces a novel approach to directed link prediction, filling a notable research gap by acknowledging the importance of the directionality of relationships often overlooked in traditional methods. We present three algorithms: an asymmetric similarity-popularity algorithm, which applies the similarity-popularity paradigm specifically to directed networks; a path exploration algorithm, which utilizes path patterns, closure probabilities, and paths’ exploratory potential to predict new links formation; and a hybrid algorithm that merges the strengths of both approaches. The effectiveness of these methods is rigorously evaluated on real-life networks, demonstrating their robust performance across various types and sizes of networked data. In addition to predictive power and runtime performance assessments, we study the impact of predicted links on network spreading capacity. This perspective provides invaluable insights into the broader implications of our algorithms on network behavior and dynamics.