Modern Graph Theory Algorithms with Python: Harness the Power of Graph Algorithms and Real-World Network Applications Using Python
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...
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| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing, Limited
2024
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| Edition: | 1st edition. |
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781805127895/?ar |
| Summary: | 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. |
| Item Description: | Description based upon print version of record. - Friendship network introduction |
| Physical Description: | 1 Online-Ressource (290 Seiten) |
| ISBN: | 9781805120179 1805120174 |
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| spelling | Farrelly, Colleen VerfasserIn aut Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python Colleen M. Farrely, Franck Kalala Mutombo ; foreword by Michael Giske 1st edition. Birmingham Packt Publishing, Limited 2024 1 Online-Ressource (290 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Description based upon print version of record. - Friendship network introduction 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. Python (Computer program language) Computer algorithms Python (Langage de programmation) Algorithmes algorithms Mutombo, Franck Kalala VerfasserIn aut Giske, Michael MitwirkendeR ctb |
| spellingShingle | Farrelly, Colleen Mutombo, Franck Kalala Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python Python (Computer program language) Computer algorithms Python (Langage de programmation) Algorithmes algorithms |
| title | Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python |
| title_auth | Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python |
| title_exact_search | Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python |
| title_full | Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python Colleen M. Farrely, Franck Kalala Mutombo ; foreword by Michael Giske |
| title_fullStr | Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python Colleen M. Farrely, Franck Kalala Mutombo ; foreword by Michael Giske |
| title_full_unstemmed | Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python Colleen M. Farrely, Franck Kalala Mutombo ; foreword by Michael Giske |
| title_short | Modern Graph Theory Algorithms with Python |
| title_sort | modern graph theory algorithms with python harness the power of graph algorithms and real world network applications using python |
| title_sub | Harness the Power of Graph Algorithms and Real-World Network Applications Using Python |
| topic | Python (Computer program language) Computer algorithms Python (Langage de programmation) Algorithmes algorithms |
| topic_facet | Python (Computer program language) Computer algorithms Python (Langage de programmation) Algorithmes algorithms |
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