Mapping of politically active groups on social networks of Russian regions (on the example of Karachay-Cherkessia Republic)
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.
In online social networks, high level features of user behavior such as character traits can be predicted with data from user profiles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis. In this study, we investigate the capabilities of previously published methods and metrics applied to the Russian online social network VKontakte. We gathered user profile data from most popular communities about suicide and depression on VK.com and performed comparative analysis between them and randomly sampled users. We have used not only standard user attributes like age, gender, or number of friends but also structural properties of their egocentric networks, with results similar to the study of suicide propensity in the Japanese social network Mixi.com. Our goal is to test the approach and models in this new setting and propose enhancements to the research design and analysis. We investigate the resulting classifiers to identify profile features that can indicate depression propensity of the users in order to provide tools for early depression detection. Finally, we discuss further work that might improve our analysis and transfer the results to practical applications.
This volume contains a selection of contributions from the "First International Conference in Network Analysis," held at the University of Florida, Gainesville, on December 14-16, 2011. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of this volume is to overcome this difficulty by collecting the major results found by the participants and combining them in one easily accessible compilation.
In this paper we propose two novel methods for analyzing data collected from online social networks. In particular we will do analyses on Vkontake data (Russian online social network). Using biclustering we extract groups of users with similar interests and find communities of users which belong to similar groups. With triclustering we reveal users’ interests as tags and use them to describe Vkontakte groups. After this social tagging process we can recommend to a particular user relevant groups to join or new friends from interesting groups which have a similar taste. We present some preliminary results and explain how we are going to apply these methods on massive data repositories.
Methods of network analysis are used in this paper for mapping the local academic community of St. Petersburg sociologists. The survey data on relations between individual scholars serve as a guide in reconstruction of the communitys network history as well as a system of independent variables in accounting for differences between its various natural zones. In this manner, the paper explores the points of convergence between Chicago school social ecology and modern social network analysis.
This volume contains two types of papers—a selection of contributions from the “Second International Conference in Network Analysis” held in Nizhny Novgorod on May 7–9, 2012, and papers submitted to an "open call for papers" reflecting the activities of LATNA at the Higher School for Economics.
This volume contains many new results in modeling and powerful algorithmic solutions applied to problems in
- vehicle routing
- single machine scheduling
- modern financial markets
- cell formation in group technology
- brain activities of left- and right-handers
- speeding up algorithms for the maximum clique problem
- analysis and applications of different measures in clustering
The broad range of applications that can be described and analyzed by means of a network brings together researchers, practitioners, and other scientific communities from numerous fields such as Operations Research, Computer Science, Bioinformatics, Medicine, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the theory and practice of network analysis. Researchers, students, and engineers from various disciplines will benefit from the state-of-the-art in models, algorithms, technologies, and techniques including new research directions and open questions.
We combine bi- and triclustering to analyse data collected from the Russian online social network Vkontakte. Using biclustering we extract groups of users with similar interests and find communities of users which belong to similar groups. With triclustering we reveal users' interests as tags and use them to describe Vkontakte groups. After this social tagging process we can recommend to a particular user relevant groups to join or new friends from interesting groups which have a similar taste. We present some preliminary results and explain how we are going to apply these methods on massive data repositories.
The current paper aims to present the Scan-4-Light study, which was conducted for the systematic scanning and analysis of the Searchlight newsletters as a rapidly growing collection of articles on trends and topics in development and poverty. Built upon the concept of the systemic foresight methodology, the Scan-4-Light approach involves the integrated use of horizon scanning, network analysis and evolutionary scenarios combined with expert consultations and workshops. The study identified the emerging trends, issues, weak signals and wild cards; created high-value visualisations to emphasize the results and findings; and produced narratives to increase the impact and awareness of the development issues. The Scan-4-Light project has resulted in a large number of specific outputs, providing the views of the Searchlight newsletters' contents at various levels of granularity. It has set out to show how the tools used here can be applied to illustrate the relationships among issues, and how these vary across countries and regions over time, and are linked to various stakeholders and possible solutions to problems. Scan-4-Light demonstrates how foresight tools and techniques can be used for the analysis of complex and uncertain issues, such as development and poverty, in a systemic way. The Scan-4-Light approach can be applied in a number of areas for scanning and identifying emerging trends and issues, and understanding the relationships between systems and solutions. The paper gives evidence that most of the issues, if not all, related to development are not isolated, but interlinked and interconnected. They require more holistic understanding and intervention with an effective collaboration between stakeholders.
The article introduces a historical-sociological research project reconstructing intellectual and institutional transformations of post-soviet social sciences in the last 25 years. The projects ambition was to achieve this aim via applying classical community study research strategy and various methods derived from social science history to the case of St. Petersburg sociologists. We identified 622 individuals as St. Petersburg sociologists and traced records of their institutional trajectories, appearance in print, citing behaviour, social networks, political attitudes, sources of income, professional authorities, and attention spaces through 25 years.