Modern Graph Theory Algorithms with Python

• Verfasst von: Farrelly, Colleen [VerfasserIn]
• Verfasst von: Mutombo, Franck Kalala [VerfasserIn]
• Titel: Modern Graph Theory Algorithms with Python
• Titelzusatz: Harness the Power of Graph Algorithms and Real-World Network Applications Using Python
• Mitwirkende: Giske, Michael [MitwirkendeR]
• Verf.angabe: Colleen M. Farrely, Franck Kalala Mutombo ; foreword by Michael Giske
• Ausgabe: 1st edition.
• Verlagsort: Birmingham
• Verlag: Packt Publishing, Limited
• Jahr: 2024
• Umfang: 1 online resource (290 p.)
• Fussnoten: Description based upon print version of record. - Friendship network introduction
• ISBN: 978-1-80512-017-9
• ISBN: 1-80512-017-4
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Python (Langage de programmation)
• Sach-SW: Algorithmes
• Sach-SW: algorithms
• K10plus-PPN: 1892759357
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m454443517X
• Lokale URL Inst.: Zum Volltext

Abstract

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.