Scalable algorithms for data and network analysis (Record no. 60855)

MARC details
000 -LEADER
fixed length control field 03645nam a22002537a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230920110437.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230920b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781680831306
028 ## - DISTRIBUTOR NAME
Distributor Name : Zafaa Books & Distributors
Distributor address : 313/56F, 49A, Anand Nagar, Inderlok, delhi
Bill Number : ZBD0148
Bill Date : 15/09/2023
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.1 TEN
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Teng, Shang-Hua.
245 ## - TITLE STATEMENT
Title Scalable algorithms for data and network analysis
Statement of responsibility, etc / Shang-Hua Teng.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc , Massachusetts
Name of publisher, distributor, etc Now Publishers
Date of publication, distribution, etc , 2016.
300 ## - PHYSICAL DESCRIPTION
Extent xi,278p.
Dimensions : 24cm.
365 ## - TRADE PRICE
Price amount 9546
505 ## - FORMATTED CONTENTS NOTE
Contents note <br/>1. Scalable algorithms<br/>1.1 Challenges of massive data<br/>1.2 The scalability of algorithms<br/>1.3 Complexity Class S<br/>1.4 Scalable reduction and algorithmic primitives<br/>1.5 Article organization 2. Networks and data<br/>2.1 Weighted graphs and affinity networks<br/>2.2 Possible sources of affinities<br/>2.3 Beyond graph models for social/information networks<br/>2.4 Basic problems in data and network analysis<br/>2.5 Sparse networks and sparse matrices 3. Significant nodes: sampling<br/>making data smaller<br/>3.1 Personalized PageRank matrix<br/>3.2 Multi-precision annealing for significant PageRank<br/>3.3 Local approximation of personalized PageRank<br/>3.4 Multi-precision sampling<br/>3.5 Significant-PageRank identification 4. Clustering: local exploration of networks<br/>4.1 Local algorithms for network analysis<br/>4.2 Local clustering and random walks<br/>4.3 Performance analysis of local clustering<br/>4.4 Scalable local computation of personalized PageRank<br/>4.5 Discussions: local exploration of networks<br/>4.6 Interplay between dynamic processes and networks<br/>4.7 Cheeger's inequality and its parameterization 5. Partitioning: geometric techniques for data analysis<br/>5.1 Centerpoints and regression depth<br/>5.2 Scalable algorithms for centerpoints<br/>5.3 Geometric separators<br/>5.4 Dimension reduction: random vs spectral<br/>5.5 Scalable geometric divide-and-conquer<br/>5.6 Graph partitioning: vertex and edge separators<br/>5.7 Multiway partition of network and geometric data<br/>5.8 Spectral graph partitioning: the geometry of a graph 6. Sparsification: making networks simpler<br/>6.1 Spectral similarity of graphs<br/>6.2 Some basic properties of spectrally similar networks<br/>6.3 Spectral graph sparsification<br/>6.4 Graph inequalities and low-stretch spanning trees<br/>6.5 Edge centrality, sampling, and spectral approximation<br/>6.6 Scalable dense-matrix computation via sparsification<br/>6.7 PageRank completion of networks 7. Electrical flows: Laplacian paradigm for network analysis<br/>7.1 SDD primitive and its scalability<br/>7.2 Electrical flow and Laplacian linear systems<br/>7.3 Spectral approximation and spectral partitioning<br/>7.4 Learning from labeled network data<br/>7.5 Sampling from Gaussian Markov random fields<br/>7.6 Scalable Newton's method via spectral sparsification<br/>7.7 Laplacian paradigm 8. Remarks and discussions<br/>8.1 Beyond graph-based network models<br/>8.2 Discussions: a family of scaling-invariant clusterability<br/>8.3 Data clustering: intuition versus ground truth<br/>8.4 Behaviors of algorithms: beyond worst-case analysis<br/>8.5 Final remarks<br/>Acknowledgements<br/>References
520 ## - SUMMARY, ETC.
Summary, etc Surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning. They also include spectral graph-theoretical methods, such as are used for computing electrical flows and sampling from Gaussian Markov random fields.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Algorithmes
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational geometry
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Graphical models
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Shang-Hua Teng.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Date checked out Price effective from Koha item type
    Dewey Decimal Classification     SNU LIBRARY SNU LIBRARY 20/09/2023 Zafaa Books 9546.00 1 005.1 TEN 29568 21/02/2024 20/09/2023 20/09/2023 Books
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