000			02251nam a22001817a 4500
999			_c1261 _d1261
005			20211125130937.0
008			211125b ||||| |||| 00| 0 eng d
020			_a9780198839972
082			_a003.75 _bNEW
100			_aNewman, M. E. J.
245			_aNetworks
250			_a2ND
260			_aNew York, NY, : _bOxford University Press, _c2018.
300			_a780 pages
505			_tThe Empirical Study of Networks. Technological networks -- _tNetworks of information -- _tSocial networks -- _tBiological networks -- _tFundamentals of Network Theory. Mathematics of networks -- _tMeasures and metrics -- _tComputer algorithms -- _tNetwork statistics and mesurement error -- _tThe structure of real-world networks -- _tNetwork Models. Random graphs -- _tThe configuration model -- _tModels of network formation -- _tApplications. Community structure -- _tPercolation and network resilience -- _tEpidemics on networks -- _tDynamical systems on networks -- _tNetwork search.
520			_aThe 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 an unprecedented scale, and the development of new theoretical tools has allowed us to extract new knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and `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 unified 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, including spectral algorithms and community detection; mathematical models of networks such as random graph models; and models of processes taking place on networks
942			_2ddc _cBK