Bridging theory and practice in network data analysis, this guide offers an intuitive
approach to understanding and analyzing complex networks. It covers foundational
concepts, practical tools, and real-world applications using Python frameworks including
NumPy, SciPy, scikit-learn, graspologic, and NetworkX.
Readers will learn to apply network machine learning techniques to real-world
problems, transform complex network structures into meaningful representations,
leverage Python libraries for efficient network analysis, and interpret network data
and results. The book explores methods for extracting valuable insights across
various domains such as social networks, ecological systems, and brain connectivity.
Hands-on tutorials and concrete examples develop intuition through visualization and
mathematical reasoning. The book will equip data scientists, students, and researchers
in applications using network data with the skills to confidently tackle network
machine learning projects, providing a robust toolkit for data science applications
involving network-structured data.