Modern Graph Theory Algorithms with Python . (Record no. 15985)
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| fixed length control field | 03483nam a2200289uu 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182906.0 |
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| fixed length control field | 250616s||||||||||||||||o||||||||||| |d |
| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781805120179 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Colleen M. Farrelly |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Modern Graph Theory Algorithms with Python . |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-06-07. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-06-07 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 290. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms </b></p><h4>Key Features</h4><ul><li>Learn how to wrangle different types of datasets and analytics problems into networks</li><li>Leverage graph theoretic algorithms to analyze data efficiently</li><li>Apply the skills you gain to solve a variety of problems through case studies in Python</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>We are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You'll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you'll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you'll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you'll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.<h4>What you will learn</h4><ul><li>Transform different data types, such as spatial data, into network formats</li><li>Explore common network science tools in Python</li><li>Discover how geometry impacts spreading processes on networks</li><li>Implement machine learning algorithms on network data features</li><li>Build and query graph databases</li><li>Explore new frontiers in network science such as quantum algorithms</li></ul><h4>Who this book is for</h4>If you're a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: Internet. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Franck Kalala Mutombo |
| Relator term | author. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Michael Giske |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Modern Graph Theory Algorithms with Python |
| Place, publisher, and date of publication | GB,Packt,2024-06-07 |
| Physical description | 290 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/473579">https://learning.packt.com/product/473579</a> |
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