Power Distribution in the Networks of Terrorist Groups: 2001–2018
Since 9/11, terrorism has become a global issue of the twenty-first century. Terrorist organizations become important actors of world politics as they gain influence on political process and decision-making. Some organizations compete with each other in order to gain more power and influence. We study the distribution of power among terrorist groups using network approach and applying classic and new centrality indices (Short-Range (SRIC) and Long-Range interactions indices (LRIC)). These indices allow to identify terrorist groups with direct and indirect influence on the terrorist network.
Trading processes is a vital part of human life and any unstable situation results in the change of living conditions of individuals. We study the power of each country in terms of produce trade. Trade relations between countries are represented as a network, where vertices are territories and edges are export flows. As flows of products between participants are heterogeneous we consider various groups of substitute goods (cereals, fish, vegetables). We detect key participants affecting food retail with the use of classical centrality measures. We also perform clustering procedure in order to find communities in networks.
This essay questions whether digital literary studies can still be meaningfully regarded as part of literary studies. This heretical question is motivated by a praxeological view of a research project for the network analysis of dramatic texts, in particular by reflecting on the project’s underlying ›epistemic thing‹, which in this case consists of specifically-formatted structural data (and not the actual primary texts themselves). What does this corpus of structural data, which was extracted from 465 plays spanning the period from 1730 to 1930, have to do with the ›epistemic things‹ of literary studies? We explore this question by providing insight into our analyses, which describe the structural evolution of the ›plays‹, try to locate ›small world‹ properties in our corpus, and develop new metrics for plot analysis. The results show not only how digital methods can supplement or enrich literary studies; they also raise questions about how digital the field of literary studies already is, since its research objects are increasingly available in digital forms.
Game theory has recently become a useful tool for modeling and studying various networks. The past decade has witnessed a huge explosion of interest in issues that intersect networks and game theory. With the rapid growth of data traffic, from any kind of devices and networks, game theory is requiring more intelligent transformation. Game theory is called to play a key role in the design of new generation networks that are distributed, self-organizing, cooperative, and intelligent. This book consists of invited and technical papers of GAMENETS 2018, and contributed chapters on game theoretic applications such as networks, social networks, and smart grid.
Using network approach, we propose a new method of identifying key food exporters based on the long-range (LRIC) and short-range interaction indices (SRIC). These indices allow to detect several groups of economies with direct as well as indirect influence on the routes of different levels in the food network.
The work is related to the detection of key international and Russian economic journals in cross-citation networks. A list of international journals and information on their cross-citations were taken from Web of Science (WoS) database while information on Russian journals was taken from Russian Science Citation Index (RSCI). We calculated classical centrality measures, which are used for key elements detection in networks, and proposed new indices based on short-range and long-range interactions. A distinct feature of the proposed methods is that they consider individual attributes of each journal and take into account only the most significant links between them. An analysis of 100 main international and 29 Russian economic journals was conducted. As a result, we detected journals with large number of citations to important journals and also journals where the observed rate of selfcitation is a dominant in the total level of citation. The obtained results can be used as a guidance for researchers planning to publish a new paper and as a measure of importance of scientific journals.
A “Network Analysis” section was arranged at the XVIIIth Interna- tional Academic Conference on Economic and Social Development at the Higher School of Economics on 11–12 April 2017. For the third year, this section invited scholars from sociology, political science, management, mathematics, and linguistics who use network analysis in their research projects. During the sessions, speakers discussed the development of mathematical models used in network analysis, studies of collaboration and communication networks, networks’ in- uence on individual attributes, identifcation of latent relationships and regularities, and application of network analysis for the study of concept networks.
The speakers in this section were E. V. Artyukhova (HSE), G. V. Gra- doselskaya (HSE), M. Е. Erofeeva (HSE), D. G. Zaitsev (HSE), S. A. Isaev (Adidas), V. A. Kalyagin (HSE), I. A. Karpov (HSE), A. P. Koldanov (HSE), I. I. Kuznetsov (HSE), S. V. Makrushin (Fi- nancial University), V. D. Matveenko (HSE), A. A. Milekhina (HSE), S. P. Moiseev (HSE), Y. V. Priestley (HSE), A. V. Semenov (HSE), I. B. Smirnov (HSE), D. A. Kharkina (HSE, St. Petersburg), C. F. Fey (Aalto University School of Business), and F. López-Iturriaga (Uni- versity of Valladolid).
In this article, our ultimate goal is to transform a graph’s adjacency matrix into a distance matrix. Because cluster density is not observable prior to the actual clustering, our goal is to find a distance whose pairwise minimisation will lead to densely connected clusters. Our thesis is centred on the widely accepted notion that strong clusters are sets of vertices with high induced subgraph density. We posit that vertices sharing more connections are closer to each other than vertices sharing fewer connections. This definition of distance differs from the usual shortest-path distance. At the cluster level, our thesis translates into low mean intra-cluster distances, which reflect high densities. We compare three distance measures from the literature. Our benchmark is the accuracy of each measure’s reflection of intra-cluster density, when aggregated (averaged) at the cluster level. We conduct our tests on synthetic graphs, where clusters and intra-cluster density are known in advance. In this article, we restrict our attention to unweighted graphs with no self-loops or multiple edges. We examine the relationship between mean intra-cluster distances and intra-cluster densities. Our numerical experiments show that Jaccard and Otsuka-Ochiai offer very accurate measures of density, when averaged over vertex pairs within clusters.
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.
The article deals with the processes of building the information society and security in the CIS in accordance with modern conditions. The main objective is to review existing mechanisms for the formation of a common information space in the Eurasian region, regarded as one of the essential aspects of international integration. The theoretical significance of the work is to determine the main controls of the regional information infrastructure, improved by the development of communication features in a rapid process.The practical component consists in determining the future policies of the region under consideration in building the information society. The study authors used historical-descriptive approach and factual analysis of events having to do with drawing the contours of today's global information society in the regional refraction.
The main result is the fact that the development of information and communication technologies, and network resources leads to increased threats of destabilization of the socio-political situation in view of the emergence of multiple centers that generate the ideological and psychological background. Keeping focused information policy can not be conceived without the collective participation of States in the first place, members of the group leaders of integration - Russia, Belarus and Kazakhstan. Currently, only produced a comprehensive approach to security in the information field in the Eurasian region, but the events in the world, largely thanks to modern technology, make the search for an exit strategy with a much higher speed. The article contributes to the science of international relations, engaging in interdisciplinary thinking that is associated with a transition period in the development of society. A study of current conditions in their relation to the current socio-political patterns of the authors leads to conclusions about the need for cooperation with the network centers of power in the modern information environment, the formation of alternative models of networking, especially in innovation and scientific and technical areas of information policy, and expanding the integration of the field in this region on the information content.
This special publication for the 2012 New Delhi Summit is a collection of articles by government officials from BRICS countries, representatives of international organizations, businessmen and leading researchers.
The list of Russian contributors includes Sergei Lavrov, Foreign Minister of Russia, Maxim Medvedkov, Director of the Trade Negotiations Department of the Russian Ministry of Economic Development, Vladimir Dmitriev, Vnesheconombank Chairman, Alexander Bedritsky, advisor to the Russian President, VadimLukov, Ambassador-at-large of the Russian Foreign Affairs Ministry, and representatives of the academic community.
The publication also features articles by the President of Kazakhstan NursultanNazarbayev and internationally respected economist Jim O’Neil, who coined the term “BRIC”. In his article Jim O’Neil speculates about the future of the BRICS countries and the institution as a whole.
The publication addresses important issues of the global agenda, the priorities of BRICS and the Indian Presidency, the policies and competitive advantages of the participants, as well as BRICS institutionalization, enhancing efficiency and accountability of the forum.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.