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Regular version of the site

Applied network research with big data and new technological advances

Priority areas of development: humanitarian
2018
Head: Kuskova, Valentina, Wasserman, Stanley Sholom

Goal of research

Development of methods and models for network data analysis, including large and temporal networks, and their testing in applied research projects of the Laboratory members in major interdisciplinary areas, such as sociology, political science, management and computer science. 

Methodology

Mathematical methods of applied network analysis constructed specially for the analysis of large and temporal networks, as well as new technological developments that allow working with data (collection, cleaning, analysis) in the most efficient way.

Empirical base of research

Data collected by the Laboratory members in their disciplinary areas – secondary data obtained from publicly available sources or scraped from web sites with laboratory’s proprietary scripts (statistics, bibliographical data, data from social networking sites), and primary data collected through mass surveys. Each research project has its own (in some cases unique) information base.

Results of research

The studies conducted by the International Laboratory`s representatives are oriented to practical application. Simultaneously, the Lab holds a number of projects in different disciplinary areas (sociology, political science, management, etc.) and thematically unrelated to each other, but unified by the implementation of common methodology of social network analysis (including its new methods developed in the Lab). In this regard, the project has resulted in the number of independent projects at different stages of implementation, with some being continued from 2017 and some being initiated in 2018.

As before, in 2018 the work on methodological developments was carried out within certain disciplinary areas. According to their research subjects, the projects can be divided into the following groups:

  • Sociology of science: the study of research teams through the analyses of bibliometric data and textual information,

  • Sociology of media and mass behavior: the study of the information dissemination and behavior of individuals in the online environments,

  • Political science: the study of political processes using statistical methods,

  • Organizational studies: the study of formal and informal groups and collectives,

  • Research in the field of computer science: the use of neural networks and artificial intelligence.

In addition to tasks motivated by the subject, the projects were aimed to solving a number of methodological problems related to the construction and development of new tools, models and methods of network analysis, as well as their application and testing on various databases through the implementation of applied research.

Each project has its own research methodology, depending on the discipline. At the same time, all Laboratory projects have in common the application of a social network analysis, which, depending on the goals and objectives of a specific project, may involve the study of ego-networks or complete network structures, dynamic (temporal), spatial, signed, or multilevel networks.

The results of the project are already published in indexed international journals on social network analysis, social sciences, and computer sciences. Some articles are going to be published in 2019. Regular discussions of the projects were held during the Lab`s Monday seminars. Results of research projects were presented to an academic and business community at the conferences (including those organized with the Laboratory) abroad and in Russia. Results of the studies can be used by a number of researchers using network methodology and working in close disciplinary areas (sociology, political sciences, management, computer science).

Level of implementation, recommendations on implementation or outcomes of the implementation of the results

The level of implementation varies among projects according to their goals. Completed projects may be implemented immediately in the spheres of sociology, political science, management and computer science as their result are published in respective outlets. 

Publications:


Karpov I., Kozhevnikov M., Kazorin V., Nemov N. Entity Based Sentiment Analysis Using Syntax Patterns and Convolutional Neural Network, in: Computational Linguistics and Intellectual Technologies: Proceedings of the Annual International Conference “Dialogue” (2016). Moscow : Изд-во РГГУ, 2016. С. 225-236. 
Fenogenova A., Karpov I., Kazorin V. A General Method Applicable to the Search for Anglicisms in Russian Social Network Texts, in: Proceedings of the Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, Saint-Petersburg, Russia, 10-12 November 2016. St. Petersburg : FRUCT Oy, 2016. С. 31-36. 
Kuskova V., Artyukhova E., Kamalov R., Danilova D. Organizational networks revisited: relational predictors of Organizational Citizenship Behavior, in: Analysis of Images, Social Networks and Texts. 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers. Communications in Computer and Information Science. Switzerland : Springer, 2017. С. 108-117. 
Maltseva D., Karpov I. Network studies in Russia: from articles to the structure of a research community, in: Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics.: Springer, 2017. С. 259-277. 
Fenogenova A., Karpov I., Kazorin V., Lebedev I. Comparative Analysis of Anglicism Distribution in Russian Social Network Texts, in: Computational Linguistics and Intellectual Technologies. International Conference "Dialogue 2017" Proceedings. Moscow : Изд-во РГГУ, 2017. С. 65-74. 
Littrell R., Kuskova V. Explicit preferred leader behaviours across cultures: Instrument development and validation // Journal of Management Development. 2018. Vol. 37. No. 3. P. 243-257. doi
Мальцева Д. В. Люблянская школа сетевого анализа: заметки социолога // Социологические исследования. 2018. № 5. C. 154-156. doi
Мальцева Д. В., Моисеев С. П. Сетевой анализ биографических интервью: кейс Т.И. Заславской // Телескоп: журнал социологических и маркетинговых исследований. 2018. № 2(128). C. 15-24. 
Мальцева Д. В. Сетевой подход как феномен социологической теории // Социологические исследования. 2018. № 4. C. 3-14. doi
Milekhina A., Artyukhova E., Kuskova V. Organizational networks revisited: Predictors of headquarters-subsidiary relationship perception, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science. Berlin : Springer, 2018. С. 39-50. 
Gradoselskaya G., Shcheglova T., Karpov I. Information Waves on Social Networks: Problematization, Definition, Distribution Mechanisms, in: 2018 Eleventh International Conference "Management of large-scale system development" (MLSD-2018). Moscow : IEEE, 2018. С. 1-4. 
Zaytsev D., Drozdova D. Mapping Paradigms of Social Sciences: Application of Network Analysis, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics. Cham : Springer, 2018. С. 235-253. 
Kalinina M., Kuskova V., Kuznetsov V. Fourty Years of Network Science: Analysis of Journal Contribution to the Field, in: Supplementary Proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts (AIST-SUP 2018), Moscow, Russia, July 5-7, 2018. Aachen : CEUR Workshop Proceedings, 2018. С. 155-160. 
Kostyakova N., Karpov I., Makarov I., Zhukov L. E. Commercial Astroturfing Detection in Social Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics. Cham : Springer, 2018. С. 309-318. 
Laptsuev R., Ananyeva M., Meinster D., Karpov I., Makarov I., Zhukov L. E. Information Propagation Strategies in Online Social Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics. Cham : Springer, 2018. С. 319-328. 
Rajput N. S., Deogune M., Mishra A., Kumar A., Makarov I. A Novel Autonomous Taxi Model for Smart Cities, in: Proceedings of 4th IEEE World Forum on Internet of Things WF-IoT 2018. New York : IEEE Computer Society, 2018. С. 625-628. 
Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Application of Graph Embedding to Constructing Graph-based Recommender System, in: Proceedings of WebSci’18 Main Conference Poster Session. Aachen : CEUR Workshop Proceedings, 2018. С. 1-2. 
Makarov I., Alisa K., Vladimir A. Fast Semi-dense Depth Map Estimation, in: Proceedings of the 2018 ACM ICMR Workshop on Multimedia for Real Estate Tech. New York : Association for Computing Machinery (ACM), 2018. С. 18-21. 
Makarov I., Diana P., Anastasia F. Improving Picture Quality with Photo-Realistic Style Transfer, in: Proceedings of 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018. Berlin : Springer, 2018. С. 47-55. 
Makarov I., Gerasimova O., Sulimov P., Zhukov L. E. Recommending Co-authorship via Network Embeddings and Feature Engineering: The case of National Research University Higher School of Economics, in: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries. New York : Association for Computing Machinery (ACM), 2018. С. 365-366. 
Makarov I., Alisa K., Vladimir A. Sparse Depth Map Interpolation using Deep Convolutional Neural Networks, in: Proceedings of 2018 41st International Conference on Telecommunications and Signal Processing (TSP). New York : IEEE, 2018. С. 1-5. 
Makarov I., Alisa K., Vladimir A. Super-resolution of interpolated downsampled semi-dense depth map, in: Proceedings of the 23rd International ACM Conference on 3D Web Technology. New York : Association for Computing Machinery (ACM), 2018. С. 1-2. 
Makarov I., Pavel P., Roman K. Voronoi-based Path Planning based on Visibility and Kill/Death Ratio Tactical Component, in: Supplementary Proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts (AIST-SUP 2018), Moscow, Russia, July 5-7, 2018. Aachen : CEUR Workshop Proceedings, 2018. С. 129-140. 
Mikhaylova O., Gradoselskaya G., Kharlamov A. Social Network Analysis of the Functional Meaning of the Term “Digital Economy”, in: 2018 Eleventh International Conference "Management of large-scale system development" (MLSD-2018). Moscow : IEEE, 2018. С. 1-3. 
Fenogenova A., Kazorin V., Karpov I., Krylova T. Automatic morphological analysis on the material of Russian social media texts, in: CLLS 2018. Computational Linguistics and Language Science. Proceedings of the Workshop on Computational Linguistics and Language Science. Moscow, Russia, April 25, 2018.: CEUR Workshop Proceedings, 2018. 
Kharlamov A. A., Gradoselskaya G., Dokuka S. Dynamic Semantic Network Analysis of Unstructured Text Corpora, in: Analysis of Images, Social Networks and Texts. 6th International Conference, 2017, Revised Selected Papers. Cham : Springer, 2018. С. 392-403. 
Zaytsev D. The riven policy style of a post-empire state: the case of Russia, in: Policy Styles and Policy-Making. Exploring the Linkages. London : Routledge, 2018. С. 289-311. 
Мальцева Д. В., Моисеев С. П. Сетевой анализ биографических интервью: кейс Т.И. Заславской, in: Смыслы жизни российской интеллигенции. Москва : РГГУ, 2018. С. ---.