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Network Algorithms, Data Mining, and Applications

NET, Moscow, Russia, May 2018

  • Conference proceedings
  • © 2020
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Overview

Editors:
  1. Ilya Bychkov

    1. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

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  2. Valery A. Kalyagin

    1. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

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  3. Panos M. Pardalos

    1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

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  4. Oleg Prokopyev

    1. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, USA

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  • Introduces state-of-the-art techniques in computer science and network analysis
  • Features new theoretical models and approaches for network analysis with new efficient tools
  • Presents a range of application for network models and network analysis

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

Included in the following conference series:

Conference proceedings info: NET 2018.

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Table of contents (17 papers)

  1. Correction to: User Preference Prediction in a Set of Photos Based on Neural Aggregation Network

    • Kirill V. Demochkin, Andrey V. Savchenko
    Pages C1-C1
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Other volumes

  1. Models, Algorithms, and Technologies for Network Analysis

  2. Computational Aspects and Applications in Large-Scale Networks

  3. Network Algorithms, Data Mining, and Applications

Keywords

About this book

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, andbiclustering algorithms are presented with applications to social network analysis.

Editors and Affiliations

  • Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Ilya Bychkov, Valery A. Kalyagin

  • Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

    Panos M. Pardalos

  • Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, USA

    Oleg Prokopyev

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