{"ID":3083940,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:38:11.424509713Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05954","arxiv_id":"2606.05954","title":"Network model selection: A review of methods","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.","short_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 met...","url_abs":"https://arxiv.org/abs/2606.05954","url_pdf":"https://arxiv.org/pdf/2606.05954v1","authors":"[\"Zoran Levnajić\"]","published":"2026-06-04T09:53:12Z","proceeding":"physics.soc-ph","tasks":"[\"physics.soc-ph\",\"cs.SI\",\"nlin.AO\",\"stat.ME\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}