Table of contents
About these proceedings
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.
nodes in empirical networks inferring network structure nonlinear dynamics on networks community detection generating random networks information diffusion
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-14683-2
- Copyright Information Springer Nature Switzerland AG 2019
- Publisher Name Springer, Cham
- eBook Packages Physics and Astronomy
- Print ISBN 978-3-030-14682-5
- Online ISBN 978-3-030-14683-2
- Series Print ISSN 2213-8684
- Series Online ISSN 2213-8692
- Buy this book on publisher's site