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Models, Algorithms, and Technologies for Network Analysis: NET 2016, Nizhny Novgorod, Russia, May 2016 Softcover reprint of the original 1st ed. 2017 [Pehme köide]

  • Formaat: Paperback / softback, 277 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 57 Illustrations, black and white; XIII, 277 p. 57 illus., 1 Paperback / softback
  • Sari: Springer Proceedings in Mathematics & Statistics 197
  • Ilmumisaeg: 02-Aug-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319860127
  • ISBN-13: 9783319860121
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  • Formaat: Paperback / softback, 277 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 57 Illustrations, black and white; XIII, 277 p. 57 illus., 1 Paperback / softback
  • Sari: Springer Proceedings in Mathematics & Statistics 197
  • Ilmumisaeg: 02-Aug-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319860127
  • ISBN-13: 9783319860121
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.
Linear Max Min Fairness in Multi-commodity Flow Networks (Hamoud Bin
Obaid, Theodore B. Trafalis).- Heuristic for Maximizing Grouping Efficiency
in the Cell Formation Problem (Ilya Bychkov, Mikhail Batsyn, Panos M.
Pardalos).- Efficient Methods of Multicriterial Optimization Based on the
Intensive Use of Search Information (Victor Gergel, Evgeny
Kozinov).- Comparison of two heuristic algorithms for a location and design
problem (Alexander Gnusarev).- A Class of Smooth Modification of
Space-Filling Curves for Global Optimization Problems (Alexey
Goryachih).- Iterative Local Search Heuristic for Truck and Trailer Routing
Problem (Ivan S. Grechikhin).- Power in network structures (Fuad Aleskerov,
Natalia Meshcheryakova, Sergey Shvydun).- Do logarithmic proximity measures
outperform plain ones in graph clustering? (Vladimir Ivashkin, Pavel
Chebotarev).- Analysis of Russian Power Transmission Grid Structure: Small
World Phenomena Detection (Sergey Makrushin).- A new approach to network
decomposition problems (Alexander Rubchinsky).- Homogeneity hypothesis
testing for degree distribution in the market graph (Semenov D.P., Koldanov
P.A.).- Network Analysis of International Migration (Fuad Aleskerov, Natalia
Meshcheryakova, Anna Rezyapova, Sergey Shvydun).- Overlapping community
detection in social networks with node attributes by neighborhood influence
(Vladislav Chesnokov).- Testing hypothesis on degree distribution in the
market graph (Koldanov P.A., Larushina J.D.).- Application of network
analysis for FMCG distribution channels (Nadezda Kolesnik, Valentina Kuskova,
Olga Tretyak).- Machine learning application to human brain network studies:
a kernel approach (Anvar Kurmukov, Yulia Dodonova, Leonid Zhukov).- Co-author
Recommender System (Ilya Makarov, Oleg Bulanov, Leonid Zhukov).- Network
Studies in Russia: From Articles to the Structure of a Research Community
(Daria Maltseva, Ilia Karpov).<