Overview
- Introduces current theories and applications in optimization methods and network models
- Contains new efficient algorithms and rigorous mathematical theories
- Features applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 197)
Included in the following conference series:
Conference proceedings info: NET 2018.
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Table of contents (19 papers)
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Front Matter
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Optimization
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Front Matter
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Network Models
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Front Matter
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Other volumes
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Models, Algorithms, and Technologies for Network Analysis
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Computational Aspects and Applications in Large-Scale Networks
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Network Algorithms, Data Mining, and Applications
Keywords
- machine learning
- multi-layered modeling
- efficient algorithms
- complex networks
- social networks
- power transmission grids
- telecommunication networks
- stock market networks
- missing node attributes
- lattice-based algorithm
- dynamic superclusters
- scheduling problem
- Spectral Partitions
- network structures
- Model Applicability
- Simulation modeling
- Network methods
- multivariate distribution
- theoretical models
- network analysis
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Models, Algorithms, and Technologies for Network Analysis
Book Subtitle: NET 2016, Nizhny Novgorod, Russia, May 2016
Editors: Valery A. Kalyagin, Alexey I. Nikolaev, Panos M. Pardalos, Oleg A. Prokopyev
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-319-56829-4
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-56828-7Published: 26 June 2017
Softcover ISBN: 978-3-319-86012-1Published: 02 August 2018
eBook ISBN: 978-3-319-56829-4Published: 23 June 2017
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: XIII, 277
Number of Illustrations: 57 b/w illustrations
Topics: Algorithms, Operations Research, Management Science, Combinatorics, Mathematical Modeling and Industrial Mathematics