Dynamics On and Of Complex Networks III

Machine Learning and Statistical Physics Approaches

  • Fakhteh Ghanbarnejad
  • Rishiraj Saha Roy
  • Fariba Karimi
  • Jean-Charles Delvenne
  • Bivas Mitra
Conference proceedings DOOCN 2017

Part of the Springer Proceedings in Complexity book series (SPCOM)

Table of contents

  1. Front Matter
    Pages i-x
  2. Network Structure

    1. Front Matter
      Pages 1-1
    2. Soumya Sarkar, Abhishek Karn, Animesh Mukherjee, Sanjukta Bhowmick
      Pages 3-21
    3. Payam Siyari, Bistra Dilkina, Constantine Dovrolis
      Pages 23-62
    4. Mary Warner, Bharat Sharma, Udit Bhatia, Auroop Ganguly
      Pages 63-79
  3. Network Dynamics

    1. Front Matter
      Pages 81-81
    2. Telmo Menezes, Camille Roth
      Pages 83-111
    3. Patrick Kasper, Philipp Koncar, Simon Walk, Tiago Santos, Matthias Wölbitsch, Markus Strohmaier et al.
      Pages 113-133
    4. Thibaud Arnoux, Lionel Tabourier, Matthieu Latapy
      Pages 135-150
  4. Theoretical Models and Applications

    1. Front Matter
      Pages 167-167
    2. Alexey N. Medvedev, Renaud Lambiotte, Jean-Charles Delvenne
      Pages 183-204
    3. Bidisha Samanta, Avirup Saha, Niloy Ganguly, Sourangshu Bhattacharya, Abir De
      Pages 205-236
  5. Back Matter
    Pages 237-244

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 (, 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

  • Fakhteh Ghanbarnejad
    • 1
  • Rishiraj Saha Roy
    • 2
  • Fariba Karimi
    • 3
  • Jean-Charles Delvenne
    • 4
  • Bivas Mitra
    • 5
  1. 1.Institute of Theoretical PhysicsTechnical University of BerlinBerlinGermany
  2. 2.Max Planck Institute for InformaticsSaarbrückenGermany
  3. 3.Department of Computational Social Science, GESISLeibniz Institute for the Social ScienceKölnGermany
  4. 4.Université Catholique de LouvainLouvain-la-NeuveBelgium
  5. 5.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Bibliographic information