Modeling and visualization of social networks

DOI: 10.31673/2412-9070.2020.022833

Abstract

In the structural approach, all network members are considered as vertices of the graph that affect the configuration of edges and other network members. The main attention is paid to the geometric shape of the network and the intensity of interactions (weight of edges), therefore, such characteristics as the mutual arrangement of vertices, centrality, transitivity of interactions are investigated.
Structural analysis and analysis of the behavior of connections in social networks is necessary in order to identify the most important peaks, communications, communities and countries, regions of the developing network. This analysis provides an overview of the global evolutionary behavior of the network. Structural and link behavior analysis uses statistical analysis methods, community definition methods, and classification algorithms.
The mutual behavior of the vertices of the network is studied based on the assumption that most of the vertices have few connections, while the nuclei (clusters) or degrees of vertices are distributed more evenly. The simulation was performed in the Social Network Visualizer environment. The behavior of vertices in clustering is studied. Weaknesses have been found to be the phenomenon that binds the network together. The effect of «small worlds» is investigated. Three states of the network are considered: a regular network, each node of which is connected to four neighboring ones, the same network in which some «close» (strong) connections are randomly replaced by «distant» (weak) connections (in this the case of the phenomenon of «small worlds»), and a random network where the number of such substitutions exceeded a certain threshold. As it turned out, it is precisely those networks whose nodes have several local and «distant» connections at the same time, showing the effect of a small world and a high level of clustering.
For the separation of communities, both specialized algorithms are used, such as the Markov clustering algorithm and simply the division of objects by modularity class.

Keywords: social networks; areas of research; theory; analysis; graph; edge; behavior; role; resources; methods; modeling; characteristics; small worlds; communities; state.

References
1. Ахрамович В. М. Модель взаємовідносин користувачів в соціальних мережах // Сучасний захист інформації. 2019. №3. С. 42–50.
2. Ахрамович В. М. Моделі довіри та репутації користувачів в соціальних мережах // Сучасний захист інформації. 2019. №4. С. 45–51.
3. Чураков А. Н. Анализ социальных сетей // СоцИс. 2001. № 1. С. 109–121.
4. Charu C. Aggarwal. Social Network Data Analytics. 2011. 520 p.
5. Milgram S. The Small World Problem // Psychology Today. 1967. Vol. 2. Р. 60–67.
6. Ланде Д. В., Фурашев В. М. Основи інформаційного і соціально-правового моделювання : монографія. Київ: ТОВ «ПанТот», 2012. 144 с.
7. Чхартишвили А. Г. Теоретико-игровые модели информационного управления. Москва: ЗАО «ПМСОФТ», 2004. 227 с.
8. Капица С. П., Курдюмов С. П., Малинецкий Г. Г. Синергетика и прогнозы будущего. Москва: Наука, 1997. 288 с.
9. Boyle A. Net not as interconnected as you think [Електронний ресурс]. URL: http://news.zdnet. com/ 2100-9595_22-502388.html.
10. Горбулін В. П., Додонов О.Г., Ланде Д. В. Інформаційні операції та безпека суспільства: загрози, протидія, моделювання: монографія. Київ: Інтертехнологія, 2009. 164 с.