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.
Arvustused
'Networks are everywhere these days. The exponential growth of network data sets demands ever more sophisticated, flexible and scalable modeling techniques. This book provides a concise introduction to basic concepts, mathematical foundations and algorithmic approaches in network analysis. Highly recommended to all practitioners, students and professionals alike.' Olaf Sporns, Distinguished Professor, Indiana University Bloomington
Muu info
Master network machine learning with practical Python examples and real-world applications.
Preface; Terminology; Part I. Foundations:
1. The network machine
learning landscape;
2. End-to-end biology network machine learning project;
Part II. Representations:
3. Characterizing and preparing network data;
4.
Statistical models of random networks;
5. Learning network representations;
Part III. Applications:
6. Applications for a single network;
7. Applications
for two networks;
8. Applications for multiple networks;
9. Deep learning
methods; Appendix A. Network model theory; Appendix B. Learning
representations theory; Appendix C. Overview of machine learning techniques;
Index.
Eric W. Bridgeford is a postdoctoral scholar in the Department of Psychology at Stanford University. Eric's background includes Computer Science, Bioengineering, and Biostatistics, and he develops methods for veridical data science. Eric is interested in biases presenting inferential obstacles to neuroscience, and how these limitations challenge analytical approaches and clinical adoption of neuroimaging methods. Alexander R. Loftus is a doctoral student in David Bau's group in the Department of Computer Science at Northeastern University, studying interpretability in deep neural networks. He has worked on implementing network algorithms in Python. He won first place in a $100,000 Kaggle competition and has published work in top AI/ML conferences. Joshua T. Vogelstein is Associate Professor of Biomedical Engineering at Johns Hopkins University. His research intersects natural and artificial intelligence, applying machine learning to biomedical challenges. He has published extensively in top scientific and AI venues, received numerous grants, and co-founded successful startups in quantitative finance and software development.
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