Modern Graph Theory Algorithms with Python
Packt Publishing Limited (Verlag)
978-1-80512-789-5 (ISBN)
Key Features
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
Book DescriptionWe 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.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
Who this book is forIf 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.
Colleen M. Farrelly is a lead data scientist and researcher with a broad industry background in machine learning algorithms and domains of application. While her focus has been industry, she also publishes academically in geometry, network science, and natural language processing. Colleen earned a graduate degree in Biostatistics from the University of Miami. Her work history includes fields like nuclear engineering, public health, biotechnology, retail, educational technology, and human behavior analytics. She previously published The Shape of Data, a comprehensive overview of machine learning from a geometric perspective. Colleen is currently focused on applications of generative models and tech education in the developing world Franck Kalala Mutombo is a Professor of Mathematics at Lubumbashi University and former Academic Director of AIMS-Senegal. He previously worked in a research position at Strathclyde University and at AIMS-South Africa in a joint appointment with the University of Cape Town. He holds a PhD in Mathematical Sciences (with focus in network science) from the University of Strathclyde, Glasgow, Scotland. His current research considers the impact of network structure on long-range interactions applied to epidemics, diffusion, object clustering, differential geometry of manifolds, finite element methods for PDEs, and data science. Currently, he teaches at University of Lubumbashi and across the AIMS Network.
Table of Contents
What is a Network?
Wrangling Data into Networks with NetworkX and igraph
Demographic Data
Transportation Data
Ecological Data
Stock Market Data
Goods Prices/Sales Data
Dynamic Social Networks
Machine Learning for Networks
Pathway Mining
Mapping Language Families – an Ontological Approach
Graph Databases
Putting It All Together
New Frontiers
| Erscheinungsdatum | 11.05.2024 |
|---|---|
| Vorwort | Michael Giske |
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-80512-789-6 / 1805127896 |
| ISBN-13 | 978-1-80512-789-5 / 9781805127895 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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