中图分类法:	N945.1 版次：
著者:	Izenman, Alan Julian,
题名:	Network models for data science : [ theory, algorithms, and applications /] / ,
出版发行:	出版地： New York : 出版社： Cambridge University Press, 出版日期： [2022]
载体形态:	xv, 484 pages : illustrations (black and white, and colour) , 1 map (colour) ; 26 cm
内容提要:	"This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component"--
主题词:	System analysis.
主题词:	Mathematical models.