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Network Models for Data Science: Theory, Algorithms, and Applications

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SKU 9781108835763 Categories ,
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This book for graduate students in statistics, data science, computer science, machine learning, and mathematics explores the theory of complex networks, modern analysis methods, and computational issues. Applications range from technology and information to finance to social ...

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Description

Product ID:9781108835763
Product Form:Hardback
Country of Manufacture:GB
Title:Network Models for Data Science
Subtitle:Theory, Algorithms, and Applications
Authors:Author: Alan Julian Izenman
Page Count:550
Subjects:Probability and statistics, Probability & statistics
Description:Select Guide Rating
This book for graduate students in statistics, data science, computer science, machine learning, and mathematics explores the theory of complex networks, modern analysis methods, and computational issues. Applications range from technology and information to finance to social science to computational biology, physics, and engineering.
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.
Imprint Name:Cambridge University Press
Publisher Name:Cambridge University Press
Country of Publication:GB
Publishing Date:2023-01-05