02839cam a2200373 i 4500 22641754 DLC 20260223144300.0 t| 220608s2023 nyu b 001 0 eng 2022022818 9781108835763 (hbk.) DLC eng rda JKRC DLC DLC-MRC DLC 23 519.6 IZE.A Izenman, Alan Julian. author 39434 Network models for data science : theory, algorithms, and applications / by Alan Julian Izenman. New York : Cambridge University Press, 2023. xv, 484 p. ; ill. (chiefly color) 26 cm. text txt rdacontent http://id.loc.gov/vocabulary/contentTypes/txt unmediated n rdamedia http://id.loc.gov/vocabulary/mediaTypes/n volume nc rdacarrier http://id.loc.gov/vocabulary/carriers/nc 59.00 Pound Includes bibliographical references and index. "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"-- Provided by publisher. System analysis http://id.loc.gov/authorities/subjects/sh85131733 39435 Mathematical models http://id.loc.gov/authorities/subjects/sh85082124 MATHEMATICS / Probability & Statistics / General bisacsh 34329 http://id.loc.gov/resources/instances/22641754 http://id.loc.gov/ontologies/bibframe/instanceOf http://id.loc.gov/resources/works/22641754 DLC bibframe2marc v3.1-dev 20260123 DLC https://github.com/lcnetdev/bibframe2marc/tree/v3.1-dev 7 cbc orignew 1 ecip 20 y-gencatlg ddc 1 23 0 613842 613842