Networks: An IntroductionThe scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of 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 a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks. The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time 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. Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks. |
Contents
1 Introduction | 1 |
The empirical study of networks | 15 |
Fundamentals of network theory | 107 |
Computer algorithms | 273 |
Network models | 395 |
Processes on networks | 589 |
| 727 | |
| 740 | |
Other editions - View all
Common terms and phrases
acyclic adjacency list adjacency matrix algorithm assortative mixing behavior breadth-first search calculate chapter citation networks clustering coefficient cocitation configuration model consider corresponding cut set defined degree distribution directed edges directed network disease distance eigenvalue eigenvector centrality elements epidemic equal equation example expected number exponential Figure fixed point function giant cluster giant component given gives groups hence in-component in-degree individuals infected instance interactions Internet Laplacian limit of large mean measure method modularity multiedges neighbors nodes number of edges number of vertices out-component out-degree pair of vertices papers percolation power-law power-law degree distribution preferential attachment probability problem protein random graph represent result self-edges shortcuts shortest path shows similar simple small components small-world model social network solution strongly connected component target tion total number tree typically undirected vector weakly connected component zero