scholarly journals Consistency landscape of network communities

2021 ◽  
Vol 103 (5) ◽  
Author(s):  
Daekyung Lee ◽  
Sang Hoon Lee ◽  
Beom Jun Kim ◽  
Heetae Kim
Keyword(s):  
Network Communities

scholarly journals Network communities within and across borders

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Federica Cerina ◽  
Alessandro Chessa ◽  
Fabio Pammolli ◽  
Massimo Riccaboni
Keyword(s):  
Network Communities

HISTORICAL MEMORY OF THE GREAT PATRIOTIC WAR IN THE COMMENTS OF «VKONTAKTE» SOCIAL NETWORKUSERS

2021 ◽  
Vol 10 (1) ◽  
pp. 12-24
Author(s):  
V.O. BEKLIAMISHEV ◽  
Keyword(s):  
Research Methodology ◽  
Historical Memory ◽  
Interdisciplinary Approach ◽  
Political Risks ◽  
Network Communities ◽  
Military Conflicts ◽  
Great Patriotic War ◽  
Rfbr Grant ◽  
High Degree ◽  
Opinion Polls

The purpose of the article is to identify the degree of presence of the Great Patriotic War theme in the network discourse and to analyze the attitude of users to the main events, personalities and forms of commemoration of this conflict. The research methodology is based on the interdisciplinary approach «Predictor Mining», which involves the analysis of Internet content for the sake of users’ behavior markers identification. The 10 largest news communities «VKontakte» (29 286 352 com-ments), as well as 7 political ones, representing the entire ideological spectrum (2 684 135 com-ments) are considered. The interim conclusions are supported by the representative opinion polls data, but its discussion is characterized by a high degree of involvement and emotional saturation. Commentators' historical perceptions are generally poor and stereotyped. The research is imple-mented at the expense of the RFBR grant «Constructing historical memory of military conflicts in network communities: basic narratives, the types of identity, political risks», project № 19-011-00833 A.

scholarly journals Graph fractal dimension and the structure of fractal networks

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Pavel Skums ◽  
Leonid Bunimovich
Keyword(s):  
Complex Networks ◽  
Evolutionary Biology ◽  
Real Life ◽  
Fractal Dimensions ◽  
Network Models ◽  
Self Similarity ◽  
Descriptive Complexity ◽  
Network Communities ◽  
Fractal Properties ◽  
Continuous Objects

Abstract Fractals are geometric objects that are self-similar at different scales and whose geometric dimensions differ from so-called fractal dimensions. Fractals describe complex continuous structures in nature. Although indications of self-similarity and fractality of complex networks has been previously observed, it is challenging to adapt the machinery from the theory of fractality of continuous objects to discrete objects such as networks. In this article, we identify and study fractal networks using the innate methods of graph theory and combinatorics. We establish analogues of topological (Lebesgue) and fractal (Hausdorff) dimensions for graphs and demonstrate that they are naturally related to known graph-theoretical characteristics: rank dimension and product dimension. Our approach reveals how self-similarity and fractality of a network are defined by a pattern of overlaps between densely connected network communities. It allows us to identify fractal graphs, explore the relations between graph fractality, graph colourings and graph descriptive complexity, and analyse the fractality of several classes of graphs and network models, as well as of a number of real-life networks. We demonstrate the application of our framework in evolutionary biology and virology by analysing networks of viral strains sampled at different stages of evolution inside their hosts. Our methodology revealed gradual self-organization of intra-host viral populations over the course of infection and their adaptation to the host environment. The obtained results lay a foundation for studying fractal properties of complex networks using combinatorial methods and algorithms.

scholarly journals Optimizing community detection in social networks using antlion and K-median

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Amany A. Naem ◽  
Neveen I. Ghali
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Population Based ◽  
Detection Problem ◽  
Network Communities ◽  
Antlion Optimization

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

scholarly journals CONSTRUCTION OF AN EMPIRICAL MODEL OF INTEGRATION PROCESSES IN INTERNET COMMUNITIES: COMBINING QUALITATIVE AND QUANTITATIVE APPROACHES

2019 ◽  
Vol 3 (4) ◽  
pp. 444-448
Author(s):  
A.M. Ponomarev ◽  
G.A. Blagodatsky
Keyword(s):  
Empirical Model ◽  
Basic Research ◽  
Russian Language ◽  
External Factors ◽  
Qualitative And Quantitative ◽  
Internet Communities ◽  
Internet Users ◽  
Network Communities ◽  
Hierarchical Structuring ◽  
Hierarchy Analysis

The article gives a brief description of the methodology, basic research methods and procedures, the purpose of which is to develop an empirical model of integration processes in Internet communities. The development was carried out in collaboration between sociologists and computer programmers. The research object is defined as network communities of Internet users based on international blog platforms on the global Internet (Russian-language segment). As a subject of research, the integration processes of these communities are highlighted. The study was carried out on the basis of the grounded-theory methodology; observation was carried out on twelve mobilizing-type Internet communities. Observation procedures were accompanied by open, axial and selective coding. The results of observation and coding were subjected to hierarchical structuring using the method of hierarchy analysis of T. Saati. The results are summarized in a four-level empirical model of integration processes in Internet communities, containing structural and functional characteristics, internal characteristics of the group and external factors, as well as indicators.

Sign in / Sign up

Export Citation Format

Share Document