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008			220608s2023 nyu b 001 0 eng
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020			_a9781108835763 (hbk.)
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082	0	0	_223 _a519.6 _bIZE.A
100	1		_aIzenman, Alan Julian. _eauthor _939432
245	1	0	_aNetwork models for data science : _btheory, algorithms, and applications / _cby Alan Julian Izenman.
260			_aNew York : _bCambridge University Press, _c2023.
300			_axv, 484 p . ; _bill. (chiefly color) _c26 cm.
336			_atext _btxt _2rdacontent _0http://id.loc.gov/vocabulary/contentTypes/txt
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365			_b59.00 _cPound
504			_aIncludes bibliographical references and index.
520			_a"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.
650		0	_aSystem analysis _0http://id.loc.gov/authorities/subjects/sh85131733 _939433
650		0	_aMathematical models _0http://id.loc.gov/authorities/subjects/sh85082124
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh _934329
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