Hands-On Network Machine Learning with Python

‘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

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.