June 2024
Intermediate to advanced
290 pages
7h 52m
English
Overview
Dive into the fascinating world of graph theory and its applications with 'Modern Graph Theory Algorithms with Python.' Through Python programming and real-world case studies, this book equips you with the tools to transform data into graph structures, apply algorithms, and uncover insights, enabling effective solutions in diverse domains such as finance, epidemiology, and social networks.
What this Book will help me do
- Understand how to wrangle a variety of data types into network formats suitable for analysis.
- Learn to use graph theory algorithms and toolkits such as NetworkX and igraph in Python.
- Apply network theory to predict and analyze trends, from epidemics to stock market dynamics.
- Explore the intersection of machine learning and graph theory through advanced neural network techniques.
- Gain expertise in database solutions with graph database querying and applications.
Author(s)
Colleen M. Farrelly, an experienced data scientist, and Franck Kalala Mutombo, a seasoned software engineer, bring years of expertise in network science and Python programming to every page of this book. Their professional experience includes working on cutting-edge problems in data analytics, graph theory, and scalable solutions for real-world issues. Combining their practical know-how, they deliver a resource aimed at both learning and applying techniques effectively.
Who is it for?
This book is tailored for data scientists, researchers, and analysts with an interest in using graph-based approaches for solving complex data problems. Ideal for those with a basic Python knowledge and familiarity with libraries like pandas and NumPy, the content bridges the gap between theory and application. It also provides insights into broad fields where network science can be impactful, contributing value to both students and professionals.
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