Abstract—Link prediction is a significant field in network science, which focuses on predicting the probability of the existence or formation of a link between nodes in a social network based on currently observed connections. Recently, several efficient link prediction algorithms have been developed, demonstrating robust results in both prediction accuracy and interpretability. However, existing research has not clearly established the relationship between network characteristics and link creation mechanisms. The ability to predict complex networks with diverse features still requires further investigation. In light of this, we introduce a novel framework designed to combine the best features of different link prediction algorithms when applied to the network, with the aim of achieving more reliable predictions about how networks will evolve in consequence. According to the proposed framework, this first focuses on the feature extraction stage. During this phase, we systematically identify and extract a comprehensive set of features from the network before moving on to the classification phase. Here, we utilize state-of-the-art ensemble learning models to assess and classify potential links within the network. By training our Machine Learning (ML) models on the extracted features, we can effectively predict whether a particular link is likely to form (positive link) or unlikely to form (negative link). The ML models were trained and evaluated using two datasets: Twitch and Facebook. Additionally, we assessed their performance on these datasets by conducting specific preprocessing and hyperparameter tuning steps. This research makes a significant contribution to improving link prediction in dynamic social networks.
 
Keywords—link prediction, hyperparameter tuning, feature extraction, ensemble learning models, directed networks


Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).