Models, Algorithms, and Technologies for Network Analysis

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.