PreprintArticleVersion 1This version is not peer-reviewed
A Powerful Approach to improve Link Prediction Accuracy in Directed Social Networks based on Ensemble Learning Models and Advanced Feature Extraction Techniques
Version 1
: Received: 29 August 2024 / Approved: 29 August 2024 / Online: 30 August 2024 (11:07:18 CEST)
How to cite:
Khan, S.; Badiy, M.; Amounas, F.; Azrour, M.; Alnajim, A.; Abdulatif, A. A Powerful Approach to improve Link Prediction Accuracy in Directed Social Networks based on Ensemble Learning Models and Advanced Feature Extraction Techniques. Preprints2024, 2024082200. https://doi.org/10.20944/preprints202408.2200.v1
Khan, S.; Badiy, M.; Amounas, F.; Azrour, M.; Alnajim, A.; Abdulatif, A. A Powerful Approach to improve Link Prediction Accuracy in Directed Social Networks based on Ensemble Learning Models and Advanced Feature Extraction Techniques. Preprints 2024, 2024082200. https://doi.org/10.20944/preprints202408.2200.v1
Khan, S.; Badiy, M.; Amounas, F.; Azrour, M.; Alnajim, A.; Abdulatif, A. A Powerful Approach to improve Link Prediction Accuracy in Directed Social Networks based on Ensemble Learning Models and Advanced Feature Extraction Techniques. Preprints2024, 2024082200. https://doi.org/10.20944/preprints202408.2200.v1
APA Style
Khan, S., Badiy, M., Amounas, F., Azrour, M., Alnajim, A., & Abdulatif, A. (2024). A Powerful Approach to improve Link Prediction Accuracy in Directed Social Networks based on Ensemble Learning Models and Advanced Feature Extraction Techniques. Preprints. https://doi.org/10.20944/preprints202408.2200.v1
Chicago/Turabian Style
Khan, S., Abdullah Alnajim and Abdulatif Abdulatif. 2024 "A Powerful Approach to improve Link Prediction Accuracy in Directed Social Networks based on Ensemble Learning Models and Advanced Feature Extraction Techniques" Preprints. https://doi.org/10.20944/preprints202408.2200.v1
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. The effectiveness of traditional algorithms can vary depending on the type of network, making some methods more suitable than others for specific scenarios. 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 for community influence analysis and anomaly detection. 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. According to the proposed framework, we first focus on the feature extraction stage. During this phase, we systematically identify and extract a comprehensive set of features from the network before moving onto 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 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. Results: Additionally, we assessed their performance on these datasets by conducting specific preprocessing and hyperparameter tuning steps. The final prediction model achieves AUC values between 94% and 99.3%. This research contributes significantly to efforts aimed at enhancing link prediction in dynamic socisal network contexts, providing valuable insights into the effectiveness of different ML algorithms in predicting future connections and enhancing our understanding of network dynamics.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
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.
