Dynamics On and Of Complex Networks III: Machine Learning and Statistical Physics Approaches

Description

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. Front Matter ....Pages i-x Front Matter ....Pages 1-1 An Empirical Study of the Effect of Noise Models on Centrality Metrics (Soumya Sarkar, Abhishek Karn, Animesh Mukherjee, Sanjukta Bhowmick)....Pages 3-21 Emergence and Evolution of Hierarchical Structure in Complex Systems (Payam Siyari, Bistra Dilkina, Constantine Dovrolis)....Pages 23-62 Evaluation of Cascading Infrastructure Failures and Optimal Recovery from a Network Science Perspective (Mary Warner, Bharat Sharma, Udit Bhatia, Auroop Ganguly)....Pages 63-79 Front Matter ....Pages 81-81 Automatic Discovery of Families of Network Generative Processes (Telmo Menezes, Camille Roth)....Pages 83-111 Modeling User Dynamics in Collaboration Websites (Patrick Kasper, Philipp Koncar, Simon Walk, Tiago Santos, Matthias Wölbitsch, Markus Strohmaier et al.)....Pages 113-133 Interaction Prediction Problems in Link Streams (Thibaud Arnoux, Lionel Tabourier, Matthieu Latapy)....Pages 135-150 The Network Source Location Problem in the Context of Foodborne Disease Outbreaks (Abigail L. Horn, Hanno Friedrich)....Pages 151-165 Front Matter ....Pages 167-167 Network Representation Learning Using Local Sharing and Distributed Matrix Factorization (LSDMF) (Pradumn Kumar Pandey)....Pages 169-181 The Anatomy of Reddit: An Overview of Academic Research (Alexey N. Medvedev, Renaud Lambiotte, Jean-Charles Delvenne)....Pages 183-204 Learning Information Dynamics in Online Social Media: A Temporal Point Process Perspective (Bidisha Samanta, Avirup Saha, Niloy Ganguly, Sourangshu Bhattacharya, Abir De)....Pages 205-236 Back Matter ....Pages 237-244