Link Prediction in Dynamic Networks Based on Topological and Historical Information

Many real-world networks are dynamic ones whose structure keeps changing over time. Link prediction in dynamic networks is more challenging and complex than that in static ones due to their dynamic nature. However, effectively using the information carried by dynamic networks can make notable enhancements to prediction accuracy. To solve the problem of dynamic network link prediction, this paper proposes a new supervised method, named THILP. This method treats link prediction as a regression problem. In this regard, both elaborate topological and historical features are extracted from multiple snapshots to represent node pairs, and the RandomForestRegressor algorithm is adopted to train a prediction model. Extensive experiments are executed on nine benchmark networks to investigate the effectiveness of the THILP method. The results show that THILP behaves remarkably better than baseline methods.