Методология против фейка
Typical errors in the planning and implementation of research using SNA are analyzed. Often these errors are not in the technical plane, but in the theoretical and methodological one. Using the example of specific research and projects, it is shown how implicit assumptions, incorrectly selected theoretical bases, uncritical use of methods and inattention when developing tools lead to the birth of semantic artifacts, "good-natured fakes".
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).
The main focus of this paper is the analysis of universities’ embeddedness into industrial sector of the Russian Northwestern region. We use webometric approach to evaluate the collaboration of universities with the use of Social Networks Analysis, as well as the examination of co-authorship network among universities and other agents. We develop our research within the framework of Triple Helix concept, taking only two agents from there: universities and companies. As a result, we found two groups of universities: which have a lot of connections with a variety of industrial and business companies and behave as key agents for the whole network as well as some with more narrowly focused types of collaboration, having fewer links with companies.
We present a novel approach to clustering Twitter users and characterizing their preferences (political or otherwise) based on the features of communication networks extracted from their tweets. We make the assumption that central users in the network, the so-called “top”, or “power” users, set the agenda, while other, “regular” users often retweet and/or mention their tweets, and behavior towards “top” users differs from the behaviour of “regular” users towards each other. We show that network clustering on Twitter can be observed more distinctively on unimodal projections of specially created bimodal networks (bipartite graphs), where top users in the networks are artificially separated into a second part according to node centrality measures. We evaluate our approach on Twitter-based datasets of mentions and retweets related to Russian political protests and a benchmark English-language Twitter dataset with distinctly polarized clusters; we compare various centrality measures and show that our algorithm yields high modularity in the resulting community structure.
During the last years, new technologies have been developing at a rapid pace; however, new technologies carry risks and uncertainties. Technology forecasting by analogy has been used in the case of emerging technologies; nevertheless, the use of analogies is subject to several problems such as lack of inherent necessity, historical uniqueness, historically conditioned awareness, and casual analogies. Additionally, the natural process of selecting the analogy technology is based on subjective criteria for technological similarities or inductive inference. Since many analogies are taken qualitatively and rely on subjective assessments, this paper presents a quantitative comparison process based on the Social Network Analysis (SNA) and patent analysis for selecting analogous technologies. In this context, the paper presents an analysis of complex patent network structures using centrality and density metrics in order to reduce the lack of information or the presence of uncertainties. The case of Autonomous Vehicles (AVs) is explored in this paper, comparing three candidate technologies which have been chosen based on the similarities with the target technologies. The best candidate technology is selected based on the analysis of two main centrality metrics (average degree and density).
The book examines the realities of the new information space, which has an active impact on social processes in the framework of digital and socio-cultural globalization. The focus is on the methodology of network analysis, which is most relevant for the study of modern information and digital society. The second section of the book contains a short dictionary of the most commonly used network terms.
Following the discussion on the role of Internet in the formation of ties across space, this paper seeks to supplement recent findings on prevalence of location-dependent preferential attachment online. We look at networks of online communities specifically aimed at development of location-independent ties. The paper focuses on the 25 largest communities of software developers in the leading Russian social networking site VKontakte, one of the communities being studied in depth. Evidence suggests that membership and friendship ties are overwhelmingly cross-city and even cross-country, while an in-depth analy-sis gives ground to assume that, commenting and liking in such communities might also be location-independent. This group case study provides some in-sights into a nature of professional networking and shows independence of the three networks: the friendship network as a means of group identification, the commenting network as an advice-giving tool, and the liking network as a result of approval by occasional visitors.