Dynamics On and Of Complex Networks III : Machine Learning and Statistical Physics Approaches (1st ed. 2019)

This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics.

It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning.

Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes. The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions.

The chapters will benefit a diverse community of researchers.

The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science.