Networks

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.

• Introduction to the Theory of Complex Systems
• Network Science
• Multilayer Networks: Structure and Function
• Linear Algebra and Learning from Data
• The Model Thinker: What You Need to Know to Make Data Work for You
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• The Hundred-Page Machine Learning Book
• Complex Network Analysis in Python: Recognize - Construct - Visualize - Analyze - Interpret
• Networks, Crowds, and Markets: Reasoning about a Highly Connected World
• Neural Networks and Deep Learning: A Textbook