Social network data usually contain different types of errors. One of them is missing data due to actor non-response. This can seriously jeopardize the results of analyses if not appropriately treated. The impact of missing data may be more severe in valued networks where not only the presence of a tie is recorded, but also its magnitude or strength. Blockmodeling is a technique for delineating network structure. We focus on an indirect approach suitable for valued networks. Little is known about the sensitivity of valued networks to different types of measurement errors. As it is reasonable to expect that blockmodeling, with its positional outcomes, could be vulnerable to the presence of non-respondents, such errors require treatment. We examine the impacts of seven actor non-response treatments on the positions obtained when indirect blockmodeling is used. The start point for our simulation are networks whose structure is known. Three structures were considered: cohesive subgroups, core-periphery, and hierarchy. The results show that the number of non-respondents, the type of underlying blockmodel structure, and the employed treatment all have an impact on the determined partitions of actors in complex ways. Recommendations for best practices are provided. © 2016 Elsevier B.V.
This chapter analyses the nature of the Brazilian socio-political protests that sparked in 2013 and are still going on today. The focus on determining the main drivers of the movement, protesters’ demands, new forms of collective action and the resulting political changes allows me to trace an important change in the Brazilian democracy as a whole. These protests are neither a one-shot deal, nor an institutionalized social movement. I argue that they rather represent a demand of protesters for participation in the permanent dialogue between the power and the public on every single issue that troubles at least some groups of the society. In this sense, such protests may indicate a completely novel era in the Brazilian democracy that renders representative democracy obsolete and insufficient, while the demands for participatory democracy are being increasingly voiced. Importantly, this mode of protesting proves rather efficient in terms of real changes in politics it brought.
Definitions: State is a type of polity that is characterized by two main dimensions: “statehood” and “stateness.” Statehood is the recognition of the state by other states as independent nation, equal to others to participate on international arena; receiving and having the “statehood” for country mean that it is a part of the “concert of nations,” such as the member of the United Nations organization. Stateness is a state capacity to sustain its territory, nation, and citizens’ welfare; it is not enough being recognized by other states as such, but also important to support this status in time
Our research focuses at the structure of a research community of Russian scientists involved into network studies, which is studied by means of analysis of articles published in Russian-language journals. The direction of network studies in Russia is quite new form of research methodology - however, in recent years we can observe the growing number of scientists working at this direction and institutionalized forms of their cooperation. Studying the structure of these researchers` community is important for the field’s development. This paper is the first report on the research, that is why it focuses on methodological issues. It covers the description of method of citation (reference) analysis that we use and the process on data collection from eLibrary.ru resource, as well as present some brief overview of collected data (based on analysis of 8,000 papers). It is concluded by representation of future steps of the research.
Organizational citizenship behavior (OCB) is an important management construct. Despite previous investigations in relation to social capital, the role of networks in its emergence has received only limited attention. In this paper we investigate the relationship between OCB, with data collected from supervisors evaluating their subordinates; several types of organizational networks (professional, friendship, support, supervisor-subordinate), and several other constructs (collected from the employees themselves), shown to affect OCB in the past. All data were collected at a large insurance company in Russia. Outcomes of this study have several important implications. First, the impact of networks on manifestation of OCB depends not only on the strength of network ties, but on types of network. Second, interorganizational relationships are complex and consist of several levels of mediated relationships. Results of this study can impact the theoretical understanding of OCB and have practical implications for the supervisor-subordinate relationships in the workplace.
In this paper we suggest the first systematic review and com- pare performance of most frequently used machine learning algorithms for prediction of the match winner from the teams’ drafts in DotA 2 computer game. Although previous research attempted this task with simple models, weve made several improvements in our approach aiming to take into account interactions among heroes in the draft. For that pur- pose we’ve tested the following machine learning algorithms: Naive Bayes classifier, Logistic Regression and Gradient Boosted Decision Trees. We also introduced Factorization Machines for that task and got our best re- sults from them. Besides that, we found that model’s prediction accuracy depends on skill level of the players. We’ve prepared publicly available dataset which takes into account shortcomings of data used in previous research and can be used further for algorithms development, testing and benchmarking.
Modern co-authorship networks contain hidden patterns of researchers interaction and publishing activities. We aim to provide a system for selecting a collaborator for joint research or an expert on a given list of topics. We have improved a recommender system for finding possible collaborator with respect to research interests and predicting quality and quantity of the anticipated publications. Our system is based on a co-authorship network derived from the bibliographic database, as well as content information on research papers obtained from SJR Scimago, staff information and the other features from the open data of researchers profiles. We formulate the recommendation problem as a weighted link prediction within the co-authorship network and evaluate its prediction for strong and weak ties in collaborative communities.
Although research collaboration has been studied extensively, we still lack understanding regarding the factors stimulating researchers to collaborate with different kinds of research partners including members of the same research center or group, researchers from the same organization, researchers from other academic and non-academic organizations as well as international partners. Here, we provide an explanation of the emergence of diverse collaborative ties. The theoretical framework used for understanding research collaboration couples scientific and technical human capital embodied in the individual with the social organization and cognitive characteristics of the research field. We analyze survey data collected from Slovenian scientists in four scientific disciplines: mathematics; physics; biotechnology; and sociology. The results show that while individual characteristics and resources are among the strongest predictors of collaboration, very different mechanisms underlie collaboration with different kinds of partners. International collaboration is particularly important for the researchers in small national science systems. Collaboration with colleagues from various domestic organizations presents a vehicle for resource mobilization. Within organizations collaboration reflects the elaborated division of labor in the laboratories and high level of competition between different research groups. These results hold practical implications for policymakers interested in promoting quality research.
The article provides an analysis of concepts of «social mass» and «mass behavior» proposed by the Soviet sociologist B. A. Grushin. The study covers the time from 1967 to 1987 when the key Grushin’s works devoted to the mass consciousness and related concepts appeared. The basis of the analysis is Grushin’s published lectures and scientific publications. The paper also includes data from studies of the history of Russian sociology, Grushin’s and his contemporaries’ memories. The author discusses factors that influenced Grushin’s scientific interests, analyzes his views of «mass» and «mass behavior» as well as their strengths and limitations. To conclude, the author states that the notions proposed by the sociologist thirty years ago are still relevant and can be used to develop the modern theory of «mass» and «mass consciousness». An essential feature of Grushin’s concept is that it makes it possible to combine ideas of his Western and domestic predecessors.
This paper deals with certain principles and examples of conducting network analysis of biographical interviews as a mixed-methods design. It reveals the general idea and special features of the network approach to studying the structure of an academic community based on a corpus of their biographical interviews. The paper focuses on the previous experience of similar research designs, summarizing the opportunities and limitations of such design. The analysis is based on the ongoing project aimed at re-constructing the interaction networks among the key figures of soviet and Russian sociology with the help of network analysis. It is based on the materials of biographical interviews (n=157) collected by B. Doktorov and implemented in the BORIS software.
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).
Why researchers that use statistics always propose hypothesis about associations between variables and try to prove cause-effect relations between them? And, vice versa, why political linguist tend to use discourse analysis to disclose cognitive manipulation by power-holders through the imposition of certain beliefs, which define the social behavior?
In this paper we propose the mapping of existed methods in social sciences, of methodological approaches which stand behind these methods and allow formulating research question, and of philosophical foundations or ontologies which explain what social reality is. Social research is driven not only by chosen methods, but also by approaches and ontology shared by researcher.
The article presents some of the results of the project "Monitoring the Russian media literacy of the population", which is being conducted by ZIRCON Research Group since 2009. The latest whole-Russian mass survey was conducted in the autumn 2015.
With the process of globalization the number of borrowings from English has rapidly increased in languages all over the world. In systems of automatic speech recognition, spell-checking, tagging and other tasks in the field of natural language processing the loan words frequently cause problems and should be treat separately. In this paper we present a corpora-based approach for the automatic detection of anglicisms in Russian social network texts. Proposed method is based on the idea of simultaneous scripting, phonetics and semantics similarity of the original Latin word and its Cyrillic analogue. We used a set of transliteration, phonetic transcription and morphological analysis methods to find possible hypotheses and distributional semantic models to filter them. Resulting list of borrowings, gathered from approximately 20 million LiveJournal texts shows good intersection with manually collected dictionary. Proposed method is fully automated and can be applied to any domain-specific area.
Analytical communities for the goal of this paper can be defined as loosely united clusters of professionals doing joint or related work in policy analysis, research and development, who frequently work together on common analytical goals and clients, while not necessarily form a special organizational structure which differ them from think tank. Examples of analytical communities could be university research departments, regular authors of one analytical journal, members of certain intellectual clubs, or regularly meeting informal research groups, including individual intellectuals working together on the regular basis. The goal of this paper is to show an important connection between regional and local analytical communities and local administrations of the Russian regions to specify a unique role the analytical communities can play in strategic planning, providing local administrations both with data, ideas, solutions, and scenarios of social developments, which local authorities are interested to get the answers to.
Social actors are often nested within multiple levels that share several members, giving rise to multimodal data. Such data are complex if the actor-nesting is not mutually exclusive. We use affiliation networks to represent teams and individuals, with links representing team membership; social relations between individuals are represented using one-mode networks. We propose an extension of correspondence analysis to multiple levels, incorporating multiple relations and attributes, and demonstrate it with two illustrative examples. We also show how results serve as an exploratory stepping-stone for generating hypotheses to be tested in a more focused manner using confirmatory techniques such as p*/ERGM.
We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their PageRank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover  used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomenon.
We consider the problem of managing a bounded size First-In-First-Out (FIFO) queue buffer, where each incoming unit-sized packet requires several rounds of processing before it can be transmitted out. Our objective is to maximize the total number of successfully transmitted packets. We consider both push-out (when a policy is permitted to drop already admitted packets) and non-push-out cases. We provide worst-case guarantees for the throughput performance of our algorithms, proving both lower and upper bounds on their competitive ratio against the optimal algorithm, and conduct a comprehensive simulation study that experimentally validates predicted theoretical behavior.