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Network Algorithms, Data Mining, and Applications

NET, Moscow, Russia, May 2018

  • Ilya Bychkov
  • Valery A. Kalyagin
  • Panos M. Pardalos
  • Oleg Prokopyev
Conference proceedings NET 2018

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 315)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Network Algorithms

    1. Front Matter
      Pages 1-1
    2. Hamoud S. Bin-Obaid, Theodore B. Trafalis
      Pages 3-18
    3. Dmitry I. Ignatov, Polina Ivanova, Albina Zamaletdinova
      Pages 19-35
    4. Pierre Miasnikof, Leonidas Pitsoulis, Anthony J. Bonner, Yuri Lawryshyn, Panos M. Pardalos
      Pages 37-48
    5. Angelo Sifaleras, Ioannis Konstantaras
      Pages 71-82
  3. Network Data Mining

    1. Front Matter
      Pages 83-83
    2. Marina Ananyeva, Ilya Makarov, Mikhail Pendiukhov
      Pages 85-99
    3. Anna Averchenkova, Alina Akhmetzyanova, Konstantin Sudarikov, Stanislav Petrov, Ilya Makarov, Mikhail Pendiukhov et al.
      Pages 101-119
    4. Kirill V. Demochkin, Andrey V. Savchenko
      Pages 121-127
    5. Fuad Aleskerov, Natalia Meshcheryakova, Sergey Shvydun
      Pages 143-160
    6. Anastasiia D. Sokolova, Andrey V. Savchenko
      Pages 161-170
  4. Network Applications

Other volumes

  1. NET 2016, Nizhny Novgorod, Russia, May 2016
  2. NET 2017, Nizhny Novgorod, Russia, June 2017
  3. Network Algorithms, Data Mining, and Applications
    NET, Moscow, Russia, May 2018

About these proceedings

Introduction

This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. 

Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.

Keywords

Network algorithms Clusters Information Theory graph dissimilarities Metaheuristics Large-Scale Graph Clustering Traveling Salesman Problem Link Partitioning Partitioning Around Medoids modeling clique relaxations Integer programming techniques Network Science Applications Large-Scale Graph Processing Systems Network data mining Machine Learning Analysis Gaussian graphical model

Editors and affiliations

  1. 1.Higher School of EconomicsNational Research UniversityNizhny NovgorodRussia
  2. 2.Higher School of EconomicsNational Research UniversityNizhny NovgorodRussia
  3. 3.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  4. 4.Department of Industrial EngineeringUniversity of PittsburghPittsburghUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-37157-9
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-37156-2
  • Online ISBN 978-3-030-37157-9
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017
  • Buy this book on publisher's site
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