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Network models for data science :theory, algorithms, and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Network models for data science :Alan Julian Izenman.
Reminder of title:	theory, algorithms, and applications /
Author:	Izenman, Alan Julian,
Description:	xv, 484 pages :illustrations (chiefly color) ;26 cm
Subject:	System analysis.
ISBN:	9781108835763$qhardcover

Network models for data science :theory, algorithms, and applications /
Izenman, Alan Julian,

Network models for data science :theory, algorithms, and applications /Alan Julian Izenman. - xv, 484 pages :illustrations (chiefly color) ;26 cm

Includes bibliographical references and indexes.

Examples of networks -- Graphs and networks; Random graph models -- Percolation on Zd -- Percolation beyond Zd -- The topology of networks -- Models of network evolution and growth -- Network sampling -- Parametric network models -- Graph partitioning I: graph cuts -- Graph partitioning II: community detection -- Graph partitioning III: Spectral clustering -- Graph partitioning IV: overlapping communities -- Examining network properties -- Graphons as limits of networks -- Dynamic networks.

"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"--

ISBN: 9781108835763$qhardcover

LCCN: 2022022818Subjects--Topical Terms:

182013
System analysis.

LC Class. No.: T57 / .I94 2023

Dewey Class. No.: 519.6

Network models for data science :theory, algorithms, and applications /
LDR:02804cam 2200361 i 4500 001 655151
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008 240606t20232023enka b 001 0 eng
010 $a 2022022818
020 $a 9781108835763$qhardcover
020 $a 1108835767$qhardcover
020 $z 9781108886666$qelectronic book
020 $z 9781108889032$qelectronic book
029 1 $a UKMGB $b 020728777
035 $a (OCoLC)1334563529
035 $a on1334563529
040 $a DLC $b eng $e rda $c DLC $d OCLCF $d UKMGB $d GRU $d YDX $d OCLCO $d BNG $d DEEMS
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050 0 0 $a T57 $b .I94 2023
082 0 0 $a 519.6 $2 23/eng/20220705
100 1 $a Izenman, Alan Julian, $e author. $3 966354
245 1 0 $a Network models for data science : $b theory, algorithms, and applications / $c Alan Julian Izenman.
264 1 $a Cambridge, United Kingdom ; $a New York, NY : $b Cambridge University Press, $c [2023]
264 4 $c ©2023
300 $a xv, 484 pages : $b illustrations (chiefly color) ; $c 26 cm
336 $a text $b txt $2 rdacontent
337 $a unmediated $b n $2 rdamedia
338 $a volume $b nc $2 rdacarrier
504 $a Includes bibliographical references and indexes.
505 0 $a Examples of networks -- Graphs and networks; Random graph models -- Percolation on Zd -- Percolation beyond Zd -- The topology of networks -- Models of network evolution and growth -- Network sampling -- Parametric network models -- Graph partitioning I: graph cuts -- Graph partitioning II: community detection -- Graph partitioning III: Spectral clustering -- Graph partitioning IV: overlapping communities -- Examining network properties -- Graphons as limits of networks -- Dynamic networks.
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"-- $c Provided by publisher.
650 0 $a System analysis. $3 182013
650 0 $a Mathematical models. $3 182479
650 6 $a Analyse de systé mes. $3 966395
650 7 $a systems analysis. $2 aat $3 966356
650 7 $a MATHEMATICS / Probability & Statistics / General. $2 bisacsh $3 603543
650 7 $a Mathematical models $v Congresses. $3 437634
650 7 $a System analysis $3 216714
776 0 8 $i ebook version : $z 9781108886666
994 $a C0 $b TWNUK