Network model selection: A review of methods

Erscheinungsjahr 2026
ISBN: 978-3-032-30447-6
Verlag: Springer

Buch, Englisch, 91 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: SpringerBriefs in Complexity

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.

Zielgruppe

Research

Autoren/Hrsg.

Levnajic, Zoran

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik

Inhaltsverzeichnis

Introduction.- Overview of the central problems in network science.- From data to networks.- Network completion.- Network archaeology.- Network simplification.- Network comparison.- Quantification of network randomness.- Hypergraphs.- Conclusions and outlook.

Über Autor(innen)

Zoran Levnajic is an Associate Professor of Computer and Information Science at the Faculty of Information Studies in Novo Mesto, Slovenia. His academic background spans several scientific fields: he obtained his undergraduate degree in physics from the University of Trieste in Italy, an MSc in dynamical systems from the University of California, Santa Barbara, in the USA, and a PhD in complex networks from the Jožef Stefan Institute in Slovenia. He is the Director of the Complex Systems and Data Science Lab, where his research focuses on diverse aspects of network science, with applications ranging from physics and neuroscience to the social sciences. In addition, he has advised numerous doctoral and MSc students and served as Head of the faculty’s doctoral school.