Computational Aspects and Applications in Large Scale Networks NET 2017 Nizhny Novgorod Russia June 2017 1st Edition by Valery A Kalyagin,Panos M Pardalos,Oleg Prokopyev,Irina Utkina ISBN 3030071634 978-3030071639

Computational Aspects and Applications in Large-Scale Networks: NET 2017, Nizhny Novgorod, Russia, June 2017 1st Edition by Valery A. Kalyagin, Panos M. Pardalos, Oleg Prokopyev, Irina Utkina – Ebook PDF Instant Download/Delivery: 3030071634, 978-3030071639

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Product details:

ISBN 10: 3030071634

ISBN 13: 978-3030071639 

Author: Valery A. Kalyagin, Panos M. Pardalos, Oleg Prokopyev, Irina Utkina

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.

Table of contents:

Part I: Network Computational Algorithms

Chapter 1: Tabu Search for Fleet Size and Mix Vehicle Routing Problem with Hard and Soft Time Windows

Chapter 2: FPT-algorithms for The Shortest Lattice Vector and Integer Linear Programming Problems

Chapter 3: The Video-Based Age and Gender Recognition with Convolution Neural Networks

Chapter 4: On Forbidden Induced Subgraphs for the Class of Triangle-Konig Graphs

Chapter 5: The Global Search Theory Approach to the Bilevel Pricing Problem in Telecommunication Networks

Chapter 6: Graph Dichotomy Algorithm and Its Applications to Analysis of Sks Market

Chapter 7: Cluster Analysis of Facial Video Data in Video Surveillance Systems Using Deep Learning

Chapter 8: Using Modular Decomposition Technique to Solve the Maximum Clique Problem

Part II: Network Models

Chapter 1: Robust Statistical Procedures for Testing Dynamics in Market Network

Chapter 2: Application of Market Models to Network Equilibrium Problems

Chapter 3: Selective Bi-coordinate Variations for Network Equilibrium Problems with Mixed Demand

Chapter 4: Developing a Model of Topological Structure Formation for Power Transmission Grids Based on the Analysis of the UNEG

Chapter 5: Methods of Criteria Importance Theory and Their Software Implementation

Chapter 6: A Model of Optimal Network Structure for Decentralized Nearest Neighbor Search

Chapter 7: Computational Study of Activation Dynamics on Networks of Arbitrary Structure

Chapter 8: Rejection Graph for Multiple Testing of Elliptical Model for Market Network

Chapter 9: Mapping Paradigms of Social Sciences: Application of Network Analysis

Part III: Network Applications

Chapter 1: Using Geometry of the Set of Symmetric Positive Semidefinite Matrices to Classify Structural Brain Networks

Chapter 2: Comparison of Statistical Procedures for Gaussian Graphical Model Selection

Chapter 3: Sentiment Analysis Using Deep Learning

Chapter 4: Invariance Properties of Statistical Procedures for Network Structures Identification

Chapter 5: Topological Modules of Human Brain Networks are Anatomically Embedded: Evidence from Modularity Analysis at Multiple Scales

Chapter 6: Commercial Astroturfing Detection in Social Networks

Chapter 7: Information Propagation Strategies in Online Social Networks

Chapter 8: Analysis of Co-authorship Networks and Scientific Citation Based on Google Scholar

Chapter 9: Company Co-Mention Network Analysis

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Tags: Valery A Kalyagin, Panos M Pardalos, Oleg Prokopyev, Irina Utkina, Computational Aspects, and Applications in, Large Scale Networks NET