Harness the power of graph algorithms and real-world network applications using Python

$9.99 ~~$35.99~~

By Colleen M. Farrelly & Franck Kalala Mutombo

Pages 290 pages

Published in Jun 2024

Product Type eBook

Edition 1st Edition

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Key benefits

Learn how to wrangle different types of datasets and analytics problems into networks

Leverage graph theoretic algorithms to analyze data efficiently

Apply the skills you gain to solve a variety of problems through case studies in Python

Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

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.

What you will learn

Transform different data types, such as spatial data, into network formats

Explore common network science tools in Python

Discover how geometry impacts spreading processes on networks

Implement machine learning algorithms on network data features

Build and query graph databases

Explore new frontiers in network science such as quantum algorithms

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ivan Jun 30, 2024

I found Colleen Farrelly and Frank Mutombo’s book on Modern Graph Theory Algorithms with Python to be a highly engaging and relevant read, especially for those interested in graph theory, Python programming, and machine learning. It is nicely written and easy to follow, with a strong beginning and finish. The first chapter explains core graph theory concepts. The book then culminates with a chapter dedicated to new applications of network science, which I found to be relevant information to share with readers. The readers have 12 additional chapters to learn how graph theory can solve various industry challenges, from ecological to clinical, financial, and spatial. Throughout the entire book, you will find well-defined coding examples. These gems show you cleverly how to implement graph-based algorithms using Python. In this sense, the book is pleasant to the reader since it wants you to succeed. An additional benefit this book offers to readers is that it gives you enough context and valuable explanations to incorporate new graph-based learning techniques in your daily work.This is the kind of book I will take off the shelf to look at whenever I need a refresher or inspiration on how to do something the right way. I gladly recommend it.

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Quincy H Aug 07, 2024

There are many great tutorials, online workflows, books, and other supporting content that can introduce you to concepts in data science and help you navigate difficult topics.Of these resources, I could not more highly recommend the work by Colleen M. Farrelly and Franck Kalala Mutombo - “Modern Graph Theory Algorithms with Python”.Have you ever wondered how social media platforms suggest friends to connect with? Or what goes into predicting stock prices? Or maybe even why some spoken and written languages seem easier to learn than others?This book provides answers to those questions - and much more!Colleen and Franck expertly interweave an engaging, culturally diverse, and inclusive narrative with high quality illustrations and step-by-step instructions for applying the discussed concepts to your own data.The Python code is presented clearly and provides a foundational introduction to newcomers as well as more advanced concepts for those adept in programing languages.I highly recommend this book! Go check it out!

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A.Y. Jun 18, 2024

Since its inception in 1730’s by Leonhard Euler, graph theory has found a wide range of applications in the study and analysis of networks arising in logistics, engineering, finance, energy, linguistics, social sciences, biology, and many more.This book, in my view, is a successful attempt to showcase many such applications through real-life examples and leveraging Python for network data analysis (e.g. using packages like NetworkX and igraph). The book offers a practical view of the tools, techniques, and models used in network analysis without getting into too much theory, distinguishing itself from classic graph theory texts. It covers use cases involving social and demographic data, transportation and network flow models, spectral clustering, spatiotemporal data, and SIR-diffusion models. In addition, the book includes chapters on graph machine learning, causal pathways and Bayesian networks, and quantum network science algorithms with a forward-looking perspective.Overall, I found the book easy to navigate, readable, and a great introduction to the study and analysis of network data using Python. The book will serve well both the beginner data scientist, as well as more advanced readers interested in such practical applications. One minor criticism is the abundant use of AI-generated cartoons as a part of the storytelling which I found occasionally distracting and at odds with otherwise python-generated displays, but that is my taste and entirely subjective!

Amazon Verified review

Amazon Customer Jul 13, 2024

"Modern Graph Theory with Python" is an essential guide for researchers and industry professionals diving into network science to analyze big data at scale. The book covers a wide range of topics, starting from transforming different data types into network structures to implementing advanced techniques like graph neural networks and graph databases using Python.Readers will learn how to leverage network science tools to tackle real-world problems such as predicting fake news spread, forecasting stock market crashes, and analyzing educational pathways for student success. The book provides comprehensive case studies that demonstrate end-to-end examples of applying network science concepts in Python.With a focus on practical applications, this book equips readers with the skills to wrangle datasets into network science problems and scale solutions effectively using Python. It is recommended for those with basic Python knowledge, including pandas and NumPy, as well as some experience working with datasets.Overall, "Modern Graph Theory with Python" is a valuable resource for anyone interested in network science and its applications in solving data analysis challenges in various fields. From understanding the basics of networks to exploring new frontiers in the field, this book provides a comprehensive and hands-on approach to mastering graph theory with Python.

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Athulya Ganapati Kandy Jun 29, 2024

Modern Graph Theory Algorithms with Python by Colleen M. Farrelly and Franck Kalala Mutombo is a comprehensive and insightful guide that bridges the gap between theoretical graph theory and practical applications using Python. This book stands out for several reasons, making it a valuable resource for both novice and experienced data scientists and programmers.Content and Structure:The book is well-structured, beginning with fundamental concepts of graph theory and progressively delving into more complex algorithms and real-world applications. The authors do an excellent job of explaining the theory behind each algorithm, followed by clear, well-commented Python implementations. This approach allows readers to not only understand the concepts but also see how they can be applied in practical scenarios.Highlights:Comprehensive Coverage: The book covers a wide range of topics, including basic graph terminology, traversal algorithms, shortest path algorithms, network flow, and graph coloring. Each topic is explained thoroughly, with a balance of theory and code examples.Practical Applications: Farrelly and Mutombo illustrate the real-world relevance of graph algorithms through diverse examples and case studies. Whether it's social network analysis, logistics, or bioinformatics, the applications demonstrate the versatility and power of graph theory.Python Integration: The use of Python for implementing the algorithms is a significant strength. The authors leverage popular libraries such as NetworkX, making it easy for readers to experiment with and extend the provided code. This practical approach helps in solidifying the reader's understanding and encourages hands-on learning.Clear Explanations: The book is written in a clear and accessible style. The authors take care to explain complex concepts in an intuitive manner, often using diagrams and step-by-step walkthroughs of algorithms. This makes the book suitable for readers with varying levels of expertise in graph theory and programming.Areas for Improvement:Depth in Advanced Topics: While the book provides a solid foundation and covers a wide array of topics, some advanced topics could benefit from deeper exploration. For readers looking for in-depth coverage of cutting-edge graph algorithms, additional resources might be necessary.Assumed Prerequisites: The book assumes a basic understanding of Python and fundamental programming concepts. While this is reasonable for the target audience, absolute beginners might find some sections challenging without supplementary learning materials.Conclusion:Modern Graph Theory Algorithms with Python is an invaluable resource for anyone looking to harness the power of graph algorithms in real-world applications. Colleen M. Farrelly and Franck Kalala Mutombo have crafted a book that is both educational and practical, making complex concepts accessible and applicable. Whether you're a data scientist, software engineer, or researcher, this book provides the tools and knowledge needed to effectively utilize graph theory in your work.Highly recommended for those eager to explore the fascinating world of graph algorithms through the lens of Python programming.

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Modern Graph Theory Algorithms with Python

Harness the power of graph algorithms and real-world network applications using Python

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