Network model selection: A review of methods

2026-06-04

AI Summary

This study addresses a central challenge in modeling the evolution of complex networks: selecting the optimal network generative model from a set of candidates. It presents the first systematic review and classification of existing model selection methods, organizing them into four categories based on their underlying principles. The work provides a comprehensive analysis of each approach’s theoretical foundations, technical implementation, and available software tools. By offering a panoramic overview of the current landscape, this research not only clarifies key methodological distinctions but also identifies promising directions for future work. Ultimately, it lays the groundwork for developing a unified and efficient framework for network model selection, serving as an essential reference for researchers in the field.

Abstract

Understanding the processes behind the evolution of complex networks is a key objective in network science. An effective framework for tackling this challenge is network model selection, which involves finding the model from a set of candidates that best explains a given network. This book is a systematic review of methods for this purpose. Each method is outlined in three parts: its core principle (used to organize methods into four categories), other relevant details including my own observations, and software availability. The book provides a comprehensive overview of the state-of-the-art in network model selection and concludes by exploring future directions. A unified, optimal method could identify the mechanisms that shape real-world networks more precisely than any current approach. This work represents the first step toward developing such an optimal method. It will be a valuable resource for students and researchers in network science.

Problem

• network model selection
• complex networks
• model comparison
• network evolution
• mechanism identification

Innovation

• network model selection
• systematic review
• model comparison
• complex networks
• network science