Screen LibraryNetwork models for data science : theory, algorithms, and applications /
"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 workin...
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| Main Authors: | |
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| Published: |
Cambridge University Press,
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| Publisher Address: | Cambridge, UK : |
| Publication Dates: | 2023. |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Summary: |
"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"-- |
| Carrier Form: | xv, 484 pages : illustrations (some color), maps ; 26 cm |
| Bibliography: | Includes bibliographical references (pages 441-473) and index. |
| ISBN: |
9781108835763 1108835767 |
| Index Number: | T57 |
| CLC: | N945.1 |
| Call Number: | N945.1/I98 |