Computational Aspects and Applications in Large-Scale Networks
NET 2017, Nizhny Novgorod, Russia, June 2017
(Sprache: Englisch)
Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this...
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Klappentext zu „Computational Aspects and Applications in Large-Scale Networks “
Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will 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.This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government.
Inhaltsverzeichnis zu „Computational Aspects and Applications in Large-Scale Networks “
Part I: Network Computational Algorithms.- Batsyn, M., Bychkov, I., Komosko, L. and Nikolaev, A: Tabu Search for Fleet Size and Mix Vehicle Routing Problem with Hard and Soft Time Windows.- Gribanov, D: FPT-algorithms for The Shortest Lattice Vector and Integer Linear Programming Problems.- Kharchevnikova, A. and Savchenko, A: The Video-Based Age and Gender Recognition with Convolution Neural Networks.- Mokeev, D. B: On forbidden Induced Subgraphs for the Class of Triangle-Konig Graphs.- Orlov, A: The Global Search Theory Approach to the Bilevel Pricing Problem in Telecommunication Networks.- Rubchinsky, A: Graph Dichotomy Algorithm and Its Applications to Analysis of Stocks Market.- Sokolova, A. and Savchenko, A: Cluster Analysis of Facial Video Data in Video Surveillance Systems Using Deep Learning.- Utkina, I: Using Modular Decomposition Technique to Solve the Maximum Clique Problem.- Part II: Network Models.- Koldanov, A. and Voronina, M: Robust Statistical Procedures for Testing Dynamics in Market Network.- Konnov, I: Application of Market Models to Network Equilibrium Problems.- Konnov, I. and Pinyagina, O: Selective Bi-coordinate Variations for Network Equilibrium Problems with Mixed Demand.- Makrushin, S: Developing a Model of Topological Structure Formation for Power Transmission Grids Based on the Analysis of the UNEG.- Nelyubin, A., Podinovski, V. and Potapov, M: Methods of Criteria Importance Theory and Their Software Implementation.- Ponomarenko, A., Utkina, I. and Batsyn, M: A Model of Optimal Network Structure for Decentralized Nearest Neighbor Search.- Semenov, A., Gorbatenko, D. and Kochemazov, S: Computational Study of Activation Dynamics on Networks of Arbitrary Structure.- Semenov, D. and Koldanov, P: Rejection Graph for Multiple Testing of Elliptical Model for Market Network.- Zaytsev, D. and Drozdova, D: Mapping Paradigms of Social Sciences: Application of Network Analysis.- Part III: Network Applications.- Belyaev, M., Dodonova, Y.,
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Belyaeva,D., Krivov, E., Gutman, B., Faskowitz, J., Jahanshad, N. and Thompson, P: Using Geometry of the Set of Symmetric Positive Semidefinite Matrices to Classify Structural Brain Networks.- Grechikhin, I. and Kalyagin, V: Comparison of Statistical Procedures for Gaussian Graphical Model Selection.- Karpov, N., Lyashuk, A. and Vizgunov, A: Sentiment Analysis Using Deep Learning.- Koldanov, P: Invariance Properties of Statistical Procedures for Network Structures Identification.- Kurmukov, A., Dodonova, Y., Burova, M., Mussabayeva, A., Petrov, D., Faskowitz, J. and Zhukov, L: Topological Modules of Human Brain Networks are Anatomically Embedded: Evidence from Modularity Analysis at Multiple Scales.- Kostyakova, N., Karpov, I., Makarov, I. and Zhukov, L. E: Commercial Astroturfing Detection in Social Networks.- Laptsuev, R., Ananyeva, M., Meinster, D., Karpov, I., Makarov, I. and Zhukov, L. E: Information Propagation Strategies in Online Social Networks.- Matveeva, N. and Poldin, O: Analysis ofCo-authorship Networks and Scientific Citation Based on Google Scholar.- Sidorov, S., Faizliev, A., Balash, V., Gudkov, A., Chekmareva, A. and Anikin, P: Company Co-Mention Network Analysis.
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Bibliographische Angaben
- 2019, Softcover reprint of the original 1st ed. 2018, XVII, 354 Seiten, 41 farbige Abbildungen, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Valery A Kalyagin, Panos M Pardalos, Oleg Prokopyev, Irina Utkina
- Verlag: Springer, Berlin
- ISBN-10: 3030071634
- ISBN-13: 9783030071639
Sprache:
Englisch
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