Models, Algorithms, and Technologies for Network Analysis

NET 2016, Nizhny Novgorod, Russia, May 2016

  • Valery A. Kalyagin
  • Alexey I. Nikolaev
  • Panos M. Pardalos
  • Oleg A. Prokopyev
Conference proceedings NET 2016
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 197)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Optimization

  3. Network Models

    1. Front Matter
      Pages 77-77
    2. Fuad Aleskerov, Natalia Meshcheryakova, Sergey Shvydun
      Pages 79-85
    3. Vladimir Ivashkin, Pavel Chebotarev
      Pages 87-105
    4. Alexander Rubchinsky
      Pages 127-152
    5. M. A. Voronina, P. A. Koldanov
      Pages 163-174
  4. Applications

    1. Front Matter
      Pages 175-175
    2. Fuad Aleskerov, Natalia Meshcheryakova, Anna Rezyapova, Sergey Shvydun
      Pages 177-185
    3. P. A. Koldanov, J. D. Larushina
      Pages 205-214
    4. Nadezda Kolesnik, Valentina Kuskova, Olga Tretyak
      Pages 215-228

About these proceedings

Introduction

This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented.

Chapters in this book cover the following topics:

  • Linear max min fairness
  • Heuristic approaches for high-quality solutions
  • Efficient approaches for complex multi-criteria optimization problems
  • Comparison of heuristic algorithms
  • New  heuristic iterative local search 
  • Power in network structures
  • Clustering nodes in random graphs
  • Power transmission grid structure
  • Network decomposition problems
  • Homogeneity hypothesis testing
  • Network analysis of international migration
  • Social networks with node attributes
  • Testing hypothesis on degree distribution in the market graphs
  • Machine learning applications to human brain network studies

 This proceeding is a result of The 6th International Conference on Network Analysis held at the Higher School of Economics, Nizhny Novgorod in May 2016. The conference brought together scientists and engineers from industry, government, and academia to discuss the links between network analysis and a variety of fields.

Keywords

machine learning multi-layered modeling efficient algorithms complex networks social networks power transmission grids telecommunication networks stock market networks missing node attributes lattice-based algorithm dynamic superclusters scheduling problem Spectral Partitions network structures Model Applicability Simulation modeling Network methods multivariate distribution theoretical models network analysis

Editors and affiliations

  • Valery A. Kalyagin
    • 1
  • Alexey I. Nikolaev
    • 2
  • Panos M. Pardalos
    • 3
  • Oleg A. Prokopyev
    • 4
  1. 1.National Research University Higher School of EconomicsNizhny NovgorodRussia
  2. 2.National Research University Higher School of EconomicsNizhny NovgorodRussia
  3. 3.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  4. 4.Department of Industrial EngineeringUniversity of Pittsburgh PittsburghUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-56829-4
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-56828-7
  • Online ISBN 978-3-319-56829-4
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017