This is the code repository for Modern Graph Theory Algorithms with Python, published by Packt.
Harness the power of graph algorithms and real-world network applications using Python
Big data demands scalable solutions. This book delves into graph-based algorithms in Python that tackle massive datasets. Using code examples, you’ll be able to leverage these techniques for big data analytics.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
#compare subgraph centrality of language families
gs=nx.subgraph_centrality(G)
print(np.mean(np.array(list(gs.values()))))
gs2=nx.subgraph_centrality(G2)
print(np.mean(np.array(list(gs2.values()))))
Following is what you need for this book:
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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
| Chapter | Software required | OS required |
|---|---|---|
| 1-14 | Python 3.12.3 | Windows, macOS, or Linux |
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.