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List of contents
- 1: Introduction
- Part I: The empirical study of networks
- 2: Technological networks
- 3: Networks of information
- 4: Social networks
- 5: Biological networks
- Part II: Fundamentals of network theory
- 6: Mathematics of networks
- 7: Measures and metrics
- 8: Computer algorithms
- 9: Network statistics and measurement error
- 10: The structure of real-world networks
- Part III: Network models
- 11: Random graphs
- 12: The configuration model
- 13: Models of network formation
- Part IV: Applications
- 14: Community structure
- 15: Percolation and network resilience
- 16: Epidemics on networks
- 17: Dynamical systems on networks
- 18: Network search
About the author
Mark Newman received a D.Phil. in physics from the University of Oxford in 1991 and conducted postdoctoral research at Cornell University before joining the staff of the Santa Fe Institute, a think-tank in New Mexico devoted to the study of complex systems. In 2002 he left Santa Fe for the University of Michigan, where he is currently Anatol Rapoport Distinguished University Professor of Physics and a professor in the university's Center for the Study of Complex Systems.
Summary
The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on an unprecedented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and central developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
Topics covered include the measurement of networks; methods for analyzing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms; mathematical models of networks, including random graph models and generative models; and theories of dynamical processes taking place on networks.
