Elements of Network Science
Description
This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.
Network science offers a means to understand and analyze complex systems that involve various types of relationships. This text bridges the gap between theoretical understanding and practical application, making network science more accessible to a wide range of users. It presents the statistical models pertaining to individual network techniques, followed by empirical applications that use both built-in and user-written packages, and reveals the mathematical and statistical foundations of each model, along with demonstrations involving calculations and step-by-step code implementation. In addition, each chapter is complemented by a case study that illustrates one of the several techniques discussed.
The introductory chapter serves as a roadmap for readers, providing an initial understanding of network science and guidance on the required packages, the second chapter focuses on the main concepts related to network properties. The next two chapters present the primary definitions and concepts in network science and various classes of graphs observed in real contexts. The final chapter explores the main social network models, including the family of exponential random graph models. Each chapter includes real-world data applications from the social sciences, using at least one of the platforms Stata, R, and Python, providing a more comprehensive understanding of the availability of network science methods across different software platforms. The underlying computer code and data sets are available online.
The book will appeal to graduate students, researchers and data scientists, mainly from the social sciences, who seek theoretical and applied tools to implement network science techniques in their work.
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Person
Dr. Antonio Zinilli is Senior Researcher at the Research Institute on Sustainable Economic Growth at the National Research Council of Italy (CNR) in Rome. He holds a PhD in Applied Social Sciences (Curriculum Quantitative Methods) from the Sapienza University of Rome. He is the coordinator of the CNR School in "Data Science: tools and methods for analysing complex Science, Technology and Innovation (STI) systems". His research, based on an interdisciplinary approach, focuses on the Science of Science, complex network models, and computational social science. He has a particular interest in the analysis and the modeling of knowledge spreading processes as well as the dynamics of R&I processes. He teaches Network Analysis and Text Mining using Python/Stata and has developed the Datanet command in Stata for Network Analysis.
Content
- 1. Introduction.- 2. Network Science: concepts and definitions.- 3. Network Metrics.- 4. Theoretical models of networks.- 5.Statistical social network models.