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.

翻译：理解复杂网络演化背后的过程是网络科学的一个核心目标。应对这一挑战的有效框架是网络模型选择，即从一组候选模型中找出最能解释给定网络的模型。本书旨在系统性地综述实现此目标的方法。每种方法均从三个部分进行概述：其核心原理（用于将方法分为四类）、其他相关细节（包括作者本人的观察）以及软件可用性。本书全面概述了网络模型选择的最新进展，并最后探讨了未来方向。一个统一的最优方法能够比现有任何方法更精确地识别塑造现实世界网络的机制。这项工作是为发展该最优方法迈出的第一步。它将成为网络科学领域学生和研究人员的重要参考资料。