With the rise of the competition in the movie production market, because of new players such as Netflix, Hulu, HBO Max, and Amazon Prime, whose primary goal is producing a large amount of exclusive content in order to gain a competitive advantage, it is extremely important to minimize the number of unsuccessful titles. This paper focuses on new approaches to predict film success, based on the movie industry community structure, and highlights the role of the casting director in movie success. Based on publicly available data we create an ``actor''-``casting director''-``talent agent'' - ``director'' communication graph and show that usage of additional knowledge leads to better movie rating prediction.
This paper presents the analysis of journals publishing articles on Social Network Analysis (SNA). The dataset consists of articles from the Web of Science database obtained by searching for “social network*”, works intensively cited, written by the most prominent authors, and published in the main SNA journals up to July 2018. There were 8,943 journals in 70,792 articles with complete descriptions. Using a two-mode network linking publications with journals and a one-mode network of citations between articles, we constructed and analysed the networks of citations and bibliographic coupling among journals. Based on the analysis of these networks, we identify the most prominent journals publishing SNA and reveal their relationships to each other. Special attention is given to the position of journal Social Networks among other journals in the field. We trace the development of some relationships through time and look at their distributions for selected journals. The results show that the field is growing, which can be seen by the annual rise of the number of journals publishing papers in SNA, and the average number of papers on SNA per journal (almost 3 in recent years). The journals which are currently present in the field belong to social and natural sciences. The social sciences group is represented mainly by journals from sociology and management. Other journals mainly come from the fields of physics, computer science, or are general scientific journals. While journals from social and computer sciences are connected with journals from the same fields, physics journals Physica A and Physical Review E have developed their own niche. SNA’s main outlet Social Networks takes a very coherent and important position. It had explicit primacy up to the 2000s; in recent years its input has declined significantly due to the large number of papers published in other journals in the field.
If missingness is encountered in a categorical regressor, which approach is preferable: complete case analysis or the missing-indicator method? The former approach implies including in analysis (linear regression in our research) only the cases without missingness across analyzed variables. This approach is embedded in many statistical applications by default, and despite the opinion that its applicability is rather restricted, up-to-date studies provide evidence for its wide applicability – even to missingness not at random. The missing-indicator method, according to which missing data are replaced with a single valid value and a new missing-indicator variable is created, pretends to be an alternative that keeps a full sample available for analysis and, hypothetically, does not lead to the deterioration of parameter estimates. By means of simulated data and a statistical experiment, controlling the factors of missingness mechanism, missingness proportion, and a regression model’s specification, we compare parameter estimates produced by each approach to handling missingness – how biased and inefficient they are. According to the results, no approach leads to crucially biased estimates, but the missing-indicator method produces ineffective estimates.
This article presents the results of a study of the experience gained from Russian organizations and enterprises’ emergency transition to remote work in the spring of 2020. The main objective of the study is to analyze the team management strategies used in the lockdown conditions in the spring of 2020: circumstances determining the effectiveness of joint work, organizational and communicative particularities, and properties of corporative culture. The transfer of employees to remote work entailed a decrease in the effectiveness of collective work with an increase in worktime. Stresses caused by concerns of people about the stability of their work proved to be justified and widespread. These concerns were partly alleviated thanks by employers’ efforts to retain workers. The authorities promptly adopted Law No. 407 on remote work. This also eased the fears of employees. Companies that managed to create workable IT systems and auxiliary services were able to quickly mobilize employees to accomplish production tasks. But teaming competencies such as problems of motivation, involvement, trust, mutual understanding and some others could not be promptly resolved. Accordingly, the crisis was surmounted successfully enough in those organizations that had these skills in the repertoire of their corporate culture. The lockdown experience shows that remote work can be a regular element of workplace relations. This requires algorithms of efficient work out of the office and the use of managerial decisions motivating employees for cooperation, trust, involvement, creativity, the ability to learn and adapt to change.
The aim of the article is to clarify the ontological foundations of the political representation within Franklin Ankersmit’s theory in the context of the modern discussion about the crisis of the liberal-democratic project and the political per se. Taking an almost unique position in this discussion, Ankersmit insists on the presence of the political in the contemporary institutions of democratic representation, resorting to the tools of the theory of aesthetics to justify his viewpoint.
The authors recognize Ankersmit’s approach as a promising direction of the reflection on possible ways out of the political crisis, but at the same time they draw attention to its inherent flaws arising from the insufficient conceptual elaboration of its political and ontological foundations. Ankersmit does not clarify how representational relationships arise and what mechanisms are responsible for maintaining the connection between the representative and the represented. Based on the aesthetic theory of Martin Heidegger, as well as the interpretations of the social contract by Carl Schmitt, Paul Ricoeur and Alexander Filippov, the authors suggest that the theory of social contract can be used as an ontological basis for Ankersmit’s concept, and show how the aesthetic approach to the political can be harmonized with this theory and why Ankersmit’s criticism of this theory is substantively inaccurate. The authors’ arguments demonstrate that the theory of social contract can be articulated in the categories of an aesthetic approach to the political and become its ontological foundation, thereby resolving certain concerns about the origin and functioning of the relations of representation in the society.
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years
This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.
Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis
Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
This book constitutes the proceedings of the 8th International Conference on Analysis of Images, Social Networks and Texts, AIST 2019, held in Kazan, Russia, in July 2019.
The 24 full papers and 10 short papers were carefully reviewed and selected from 134 submissions (of which 21 papers were rejected without being reviewed). The papers are organized in topical sections on general topics of data analysis; natural language processing; social network analysis; analysis of images and video; optimization problems on graphs and network structures; analysis of dynamic behaviour through event data.
This volume contains the refereed proceedings of the 8th International Conference on Analysis of Images, Social Networks, and Texts (AIST 2019). The previous conferences during 2012–2018 attracted a significant number of data scientists – students, researchers, academics, and engineers working on interdisciplinary data analysis of images, texts, and social networks.
Symbolic data analysis is based on special descriptions of data known as symbolic objects (SOs). Such descriptions preserve more detailed information about units and their clusters than the usual representations with mean values. A special type of SO is a representation with frequency or probability distributions (modal values). This representation enables us to simultaneously consider variables of all measurement types during the clustering process. In this paper, we present the theoretical basis for compatible leaders and agglomerative clustering methods with alternative dissimilarities for modal-valued SOs. The leaders method efficiently solves clustering problems with large numbers of units, while the agglomerative method can be applied either alone to a small data set, or to leaders, obtained from the compatible leaders clustering method. We focus on (a) the inclusion of weights that enables clustering representatives to retain the same structure as if clustering only first order units and (b) the selection of relative dissimilarities that produce more interpretable, i.e., meaningful optimal clustering representatives. The usefulness of the proposed methods with adaptations was assessed and substantiated by carefully constructed simulation settings and demonstrated on three different real-world data sets gaining in interpretability from the use of weights (population pyramids and ESS data) or relative dissimilarity (US patents data).
This conclusion presents some closing thoughts on the concepts covered in the preceding chapters of this book and makes additional suggestions regarding potential future work. The book presents a wide variety of approaches and methods related to network clustering. It suggests that the institutional structure of science has a very large impact on the generation of scientific knowledge and the generation of scientific citation networks. The book advocates the optimization approach to the clustering problem. Using an appropriate criterion function, people can express their clustering goals, including the reduction of complexity, understanding network structures, and modeling networks. Together, optimization and the sought goals help define the nature of a “good” clustering. The book finishes by stressing two very general ideas. One is the importance of the exchange of ideas between different approaches with the goal of strengthening them. The second is the coupling of network processes and network structures to help readers understand both.
In this paper we provide the methodology for evaluating ef- fectiveness of international sanctions using Data Envelopment Analysis (DEA), which we use for generating the network matrix for further anal- ysis. DEA is a non-parametric technique used to compare performance of similar units, such as departments or organizations. DEA has wide applications in all industries, and has been successfully used to compare performance of hospitals, banks, universities, etc. The most important advantage of this technique is that it can handle multiple input and out- put variables, even those not generally comparable to each other. We use the ”Threat and Imposition of Sanctions (TIES)” Data 4.0 for analysis. This database contains the largest number of cases of international sanctions (1412 from the years 1945-2005) imposed by some countries on others, takes into account simultaneous sanction imposition, and also estimates the cost of all sanctions - both for those who receive and those who impose them. As input variables for DEA model we use the impact of sender commitment, anticipated target and sender economic costs, and actual target and sender economic costs. As the output variable, we use the outcome of sanctions for senders. We describe how to use DEA cross-efficiency outputs to build the network of sanction episodes. Our proposed combination of DEA and network methodology allows us to cluster sanction episodes depending on their outcomes, and provides explanations of higher efficiency of one group of sanction episodes over the others.
In this paper we describe the Data Envelopment Analysis (DEA) research design and its applications for effectiveness evaluation of company marketing strategies. We argue that DEA is an efficient instrument for use in academia and industry to compare a company’s business performance with its competitors’. This comparison provides the company with information on the closest competitors, including evaluating strategies with similar costs, but more efficient outcomes (sales). Furthermore, DEA provides suggestions on the optimal marketing mix to achieve superior performance.
An innovative development based on the use of modern media and communication technologies requires a certain level of competence in how to use such technologies. These competencies are united by the concept of “information literacy”, proposed by Paul Gilster in 1997. The tradition of studying digital literacy in Russia is the subject of the following chapter. The different approaches to understanding digital literacy are as follows: ICT, psychological and pedagogical, media and information and industrial approaches.
Special attention is paid to the four-component digital literacy model, proposed in the framework of the project by ROCIT and the Higher School of Economics. This model is based on two substantial oppositions: firstly, the opposition “technical-technological/socio-humanitarian” and, secondly, the opposition “opportunities/threats”. It was used to construct the Index of Digital Literacy in the Russian Regions, measured since 2015.
The results of a series of media literacy measuring surveys by the ZIRCON Group from 2009–2016 are also presented.
Different research traditions have developed over time to study the quantitative aspects of information and knowledge production, such as scientometrics, bibliometrics, librametrics, informetrics, cybermetrics, webometrics, or altmetrics. These information metrics, or iMetrics, as they were labeled by Milojević and Leydesdorff in Scientometrics 95(1):141–157, 2013, are unified by the usage of quantitative data analysis, applying various statistical methods and techniques and are often used to supplement and complement each other. Representing different research traditions, they jointly form a common research field, a “discipline with many names”. In this article, we look at the development of iMetrics field and its evolution over time using bibliometric network analysis and identify its common basis, formed by the most important publications, journals, scholars and topics. The dataset consists of articles from the Web of Science database (26,414 records with complete descriptions). Analyzing the citation network, we evaluate the field’s growth and identify the most cited works. Using the Search path count (SPC) approach, we extract the Main path, Key routes paths, and Link islands in the citation network. The results show that in the last forty years the number of published papers increased, and it doubles every 8 years; the number of single author papers dropped from 50 to 10 %, and the number of papers authored by 3 or more authors is increasing. We make the conclusions about the field’s development and its current state. We also present the main authors, journals and keywords from the field, which form its common basis.
The title of the book refers to the sociological survey, conducted by the "Public opinion" Fund in 2000. It is focused on the representation of Internet as a complex phenomenon in modern Russia. First, the Internet is considered as part of the media system that not only rapidly developing, but also significantly transforming the system as a whole. Second, it contains the analysis of main online markets in Russia. Thirdly, the Internet is analyzed in political, social and cultural contexts.
The article shows, which segments constitute social and political activity in online social networks in the Karachay-Cherkessia Republic (KChR) and the width of their representation. The author's technique allows to collect data on politically active groups of KChR. The segments of social and political activity of the Republic on the social networks are shown. Eight main clusters of political activity in social networks of KChR were obtained by the author's method of grain clustering. Each cluster was analyzed by social network analysis methods. The most influential persons and social movements are shown, and features of their network activity were investigated.
The paper analyzes speech markers and semantic concepts typical for patriotic and oppositional discourse in social networks. About 100 000 posts from Facebook, VKontakte, and LiveJournal were analyzed, and 35 000 most frequent speech markers were processed, of which 1800 markers were selected for analysis. The alternative method to tf-idf metric for specific text markers identification is proposed. The features of oppositional discourse in comparison with the patriotic discourse were formulated. On the one hand, the analysis of sets of speech markers that characterize political groups allows us to understand social models and attitudes embedded in the discourse and the subsequent behavior of representatives of these groups. On the other hand, it is possible to extend a set of keywords for text search of a certain political orientation, based on the obtained results.