Optimizing machine learning for network inference through comparative analysis of model performance in synthetic and real-world networks

Optimizing machine learning for network inference through comparative analysis of model performance in synthetic and real-world networks

Abstract Understanding the structural and operational characteristics of complex systems is crucial for network science research and analysis. To better understand the dynamics and behaviors of networks, it involves studying them in a variety of settings, including social, biological, and technical...

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Bibliographic Details
Main Authors: Ruby Khan, Sumbal Khan, Bakht Pari, Krzysztof Puszynski
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Network science
Machine learning
Network inference
Logistic regression
Random forest
Scale-free networks
Online Access:https://doi.org/10.1038/s41598-025-02982-0
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