Network science is the field dedicated to the investigation of complex systems by representing them as networks. We model such networks as graphs: sets of nodes connected by edges. This deceptively simple object is the starting point of never-ending complexity, as it can represent almost every facet of reality.

This book aims at providing an introductory access to the large and interdisciplinary analytic toolbox necessary to master network science: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, ... In this second edition, you will learn more about how to deal with uncertain network data and about graph neural networks. This is an "Atlas", because it will not make you a specialist in using any of these techniques. After reading this book, you will have a general understanding about the existence of all these approaches.