Projects and Research Work
Students are invited to participate in current research projects of the International Laboratory for Applied Network Research. The projects aimed on developing and testing new methods for network data analysis. As well students have a possibility of participating in disciplinary empirical research projects of Laboratory members.
For involvement in current research activities, please contact the laboratory staff.
Below is a list of current projects:
Social Network Analysis: the Development of the Field
Since its inception, Social Network Analysis (SNA) has moved from a fragmented direction represented by the works of individual scientific groups unrelated to each other, to a discipline whose representatives by 1990 have formed an “invisible college” and achieved the status of what Kuhn had labeled a “normal science”. Starting from that time, the field has grown significantly, which can be seen by the number of scientific publications in different scientific fields, including Natural Sciences, which lead to the so called “physicists` invasion” into SNA and resulted with the development of Network Science discipline. This calls into a question whether the field remains unified and which scientific groups (by disciplines, thematic agenda, etc.) it is currently formed of.
The aim of this project is to trace the evolution of the field of Social Network Analysis using bibliographic approach. The study is based on the analysis of articles from the Web of Science Clarivate Analytics data base containing the key word “social network*”, as well as those published in the main journals in the field. The first part of the data set was collected in 2008, and the collection of networks obtained out of it was labeled SN5 and was used as a challenge data for the Sunbelt 2008 Vizards Session (7,000+ papers). The second part consisting of 60,000+ papers published in the last ten years was collected this year. Thus, the updated data set which is being analyzed includes 70,000+ publications.
The provided methodology already proved to be productive in a set of studies of different scientific fields and topics (Kejzar et al., 2010, Batagelj et al., 2014, 2017, etc.). It allows analyzing networks of co-authorship and citation, key-words co-occurrence and collaboration between journals, and allocating main key publications and actors (persons, institutions, research groups, journals) in the field of Social Network Analysis, as well as main topics and scientific ideas, connections between them and their evolution through time.
The study will contribute to the research in the field of sociology of science and those focused on the SNA filed development.
Network research in Russia: the structure of the scientific community
Currently, the laboratory has collected a unique database containing information about all articles published in the scientific electronic library eLibrary.ru (more than 11,500 articles), with bibliometric information about the authors. Despite the fact that the work on citation networks has been going on for a long time and in different directions, we have now developed a unique analysis method that takes into account temporal changes as well. The result of the work will be a series of publications that will be interesting not only from a substantive, but also from a methodological point of view.
Methodology for Measuring Polarization of Political Discourse: Case of Comparing Oppositional and Patriotic Discourse in Online Social Networks
The project 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.
Information Waves on Social Networks: Problematization, Definition, Distribution Mechanisms
The problem of studying information waves is underestimated by researchers of the social nature of this phenomenon. This project fills this gap by conceptualizing the notion of “information wave”. Information waves in social networks have a dual nature: on the one hand, having a mathematical distribution graph (wave profile), on the other – a discrete-network distribution map. The project shows examples of analysis of information waves and both kinds of their presentation. We are conducting analysis of information waves on specific socio-political examples.
Mapping of Politically Active Groups on Social Networks of Russian Regions (on the Example of Karachay-Cherkessia Republic)
The project studies which segments constitute social and political activity in online social networks in the Karachay-Cherkessia Republic (KChR), and how widely they are represented. Our technique allow to collect data of politically active groups of the KChR is developed. It is shown of what segments consists the social and political activity of the republic on the social networks Facebook, VKontakte and some others. We use our method of seed clustering to reveal main clusters of political activity in social networks of KChR.
Text analysis of online-networks texts using deep learning methods
The goal of the project is to investigate the possibility of creating a prototype of text processing tools derived from the recognition of spontaneous speech or written by users of social networking resources.
In 2018, a number of works on the first phase of this project were completed, including:
1. research of existing and development of new methods and tools for analyzing morphological, grammatical, stylistic and contextual differences of network texts from traditional written language;
2. the establishment of lexical, morphological, syntactic, idiosyncratic, content-specific and structural features inherent in network texts, and to determine the areas of their variations;
3. study the possibility of improving the quality analysis of the tonality of network texts.
In 2019, it is planned to continue work on the project with the expansion of analysis in other languages.
Social Mechanisms of the Subject Area Formation. The Case of “Digital Economy”
The structure of natural language could be considered a social network. This implies allocation of the speech markers, which describe the subject and semantic areas. In this project a wide range of texts about digital economy was analyzed, making it possible to show the thematic structure of this subject area. Central and peripheral concepts were identified to characterize theoretical core concepts and related topics clarifying the application of digital economy. Identification of the thematic areas was performed in two ways – through the construction of a thematic tree through neural network modeling in the Text Analyst and through the analysis of semantic networks. The results, approaches and methods of this study could be used during the investigation of the other large thematic fields related to new ideological currents being developed as an element of social design and management.
Mixed Methods in Social Network Analysis: Combining Quantitative and Qualitative Approaches
Recently, mixed methods as a research design have become popular in the social sciences. However, there is a discussion about its novelty - the combination of different approaches is quite a usual way of doing research in sociology. Answering the critique, evangelists of mixed methods approach point out that the ‘real’ MM research should be based on integration of methods not only in data collection, but also in interpretation, and contain all the indicators of validity for qualitative and quantitative parts.
Social network analysis was institutionalized as a quantitative methodology, which uses different formal statistical and graph metrics to calculate the relationships between different actors (people, their groups or organizations) represented as networks. At the same time, there is also a tradition of qualitative approach in social network analysis. Even though originally this approach was created in 50th by social anthropologists, its development took place in late 80-90th, together with the stream of "cultural turn" in sociology and the appearance of the field called "relational sociology". Both of these perspectives have their own settled approaches to network data collection, analysis and interpretation.
Another direction of the SNA development is the integration of quantitative and qualitative approaches, which corresponds to the ‘mixed-methods strategy’ of research. The main idea of this integration is to consider the ‘dual nature of social reality’ by focusing both on network structures of relations and external contexts, on the one hand, and internal individual meanings of these relations, on the other. However, in applying the theoretical ideas of MM in practice, there arise many methodological issues on data collection, storage, analysis and interpretation.
This project is, first, propose a theoretical and methodological frame for further discussion on MM in SNA and discuss different characteristics that a research should have in the MM design. Second, it will focus on the problems associated with mixing methods in network studies.
Network Analysis of Countries’ Mutual Attraction: Commonwealth of Independent States’ Case
The relations between countries can be considered as a network, where the countries are nodes and the thickness of links is the indicator of their attraction or repulsion (depending on the sense of relation). In our project, we consider the relations between countries-members of the Commonwealth of Independent States (CIS), which was built as a result of the USSR collapse. These countries are Armenia, Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, and Uzbekistan.
The data is taken from the project “Integration Barometer”, implemented by the Eurasian Development Bank Centre for Integration Studies in partnership with the International Research Agency Eurasian Monitor. Since 2012, six waves of measurement of the public mood in the post-Soviet space were conducted. The focus of research was attitudes towards integration shown by citizens of Eurasian Economic Union (EAEU) member states and other countries. The amount of countries varies from 12 (2013) to 7 (2017). Each year in each country at least 1,000 people were included into representative national samples.
Among others, the questionnaire contains questions about people`s integration preferences in different spheres of life – social, economic and political. Respondents are asked about the countries from the CIS where they have close contacts (relatives, friends), would like to travel, send their children to study, or migrate; which country they think their country should have trade relations with, which one would support their country in political sense, etc. Such information is presented in the form of integrative matrixes on issues of communication, migration and general attraction (which is average-weighted numbers of references), out of which networks can be constructed.
We can compute various metrics for individual nodes (countries) and network as a whole. We identify the most central countries, which were selected by the largest number of respondents in the other countries (in-degree centrality), or where the largest number of respondents selected the other countries more frequently (out-degree centrality). The analysis also includes clustering, which allows us to get information on mostly connected countries of post-Soviet space, oriented to each other. The data in different waves help us to trace the dynamics of attraction changing.
Regional Migration in Europe from a Network Perspective
This study analyzes patterns of intra-country regional migration in Europe. Using network analysis, we study structures and properties of interregional migration networks, their similarities and diversity across the countries, and their evolution over time.
Despite a wide spread of theoretical literature on the location choice of individuals, there is still a deficiency of empirical studies. The new economic geography models stress a strong effect of an access to a market on individuals’ migration choices (Krugman 1991). Many studies have shown that the labor migration between regions is related to market potentials of these regions and a real wage disparity between them. However, some recent empirical investigations based on a number of European countries show an insignificant effect of expected wages in receiving regions on agent’s decision to migrate there (Crozet 2004), while in other countries a wealth improvement is a core attractive force for migration decisions.
This study attempts to recognize differences in a formation of regional migration flows among countries through a network perspective. To realize our project we use different networks analysis techniques, which allow revealing a topology and evolution of interregional migration networks. For our analysis we use “Eurostat” dataset on bilateral migration flows among NUTS2 regions in 14 European countries for a period 2000-2007.
Regional Migration in Russia from a Network Perspective
In this study, we are analyzing patterns of regional migration in Russian. We use migration data provided by Russian Federal State Statistics Service. Because of the structure of the data, we have complete or very dense weighted directed networks where all nodes are interconnected. These networks do not allow capturing the pattern of regional migration in full details. To overcome this problem, we use methods of reducing weighted complete networks. Mainly, we are testing and comparing the minimum spanning tree algorithm and algorithm of sequential cutting of weak ties. After applying the algorithm, we are transforming resulting networks to their binary projections. This strategy corresponds to recent findings, which claim what the characteristics of a network, calculated on its binary projection can be even more informative rather than these characteristics calculated on an initial weighted network.