Fuad Aleskerov – författare

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes, e.g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally, it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus, critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks, international migration, global trade, global food network, arms transfers, networks of terrorist groups, and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas, the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance, migration, trade, etc.) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github.com/SergSHV/slric.

Clusters, Orders, and Trees: Methods and Applications

The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data.This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions.

This book describes a new holistic methodology for analyzing a field of scientific literature using a combination of network and semantic analysis of bibliometric data, in order to identify the main citation patterns and most impactful research in a scientific field. It introduces new centrality indices that take into account more complex parameters of nodes than classical indices, such as group influence and the influence of pivotal vertices on other vertices. Other topics covered by the book include pattern analysis and stability metrics; centrality analysis of article citation networks; journal citation networks; and methods of semantic analysis to analyze trends. The book also shows how to analyze the co-occurrence of terms and to learn more about research trends by dividing articles into topical groups. While the scientific literature of Parkinson’s disease (PD) forms the basis of this book, its contents will be useful to researchers in any scientific domain, as well as journal editorial teams, scientific organizations, and investors.

The utility maximization paradigm forms the basis of many economic, psychological, cognitive and behavioral models. Since it was first devised in the eighteenth century, numerous examples have revealed the deficiencies of the concept. This book makes a contribution to overcome those deficiencies by taking into account insensitivity of measurement threshold and context of choice. It covers classic theory as a special, context-free case and gives a systematic overview of new models of utility maximization within a context-dependent threshold as well as related preference and choice models. The second edition has been updated to include the most recent developments and a new chapter on classic and new results for infinite sets. The presented models will be helpful to scientists in economics, decision making theory, social choice theory, behavioral and cognitive sciences, and related fields.