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.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 Netwlã�