語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ subject:"Analyse de systé mes." ]
切換:
標籤
|
MARC模式
|
ISBD
Network models for data science :theory, algorithms, and applications /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Network models for data science :Alan Julian Izenman.
其他題名:
theory, algorithms, and applications /
作者:
Izenman, Alan Julian,
面頁冊數:
xv, 484 pages :illustrations (chiefly color) ;26 cm
標題:
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
003
OCoLC
005
20240606103739.0
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
042
$a
pcc
049
$a
NUKM
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
筆 0 讀者評論
全部
西方語文圖書區(四樓)
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
320000742876
西方語文圖書區(四樓)
1圖書
一般圖書
T57 I98 2023
新書使用(New Book)
在架
0
1 筆 • 頁數 1 •
1
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入