The Model of Secure Social Networks Activity Based on Graph Theory
Pavlo Shchypanskyi1, Vitalii Savchenko2, Volodymyr Akhramovych3, Tetiana Muzshanova4, Svitlana Lehominova5, Volodymyr Chegrenets6
1Pavlo Shchypanskyi*, PhD, Professor, Deputy Head of Ivan Cherniakhovskyi National Defense University of Ukraine, Kyiv, Ukraine.
2Vitalii Savchenko*, Doctor of Technical Science, Professor, Director of the Institute of Information Protection, State University of Telecommunications, Kyiv, Ukraine.
3Volodymyr Akhramovych, PhD, Associated Professor, Institute of Information Protection, State University of Telecommunications, Kyiv, Ukraine.
4Tetiana Muzhanova, PhD in Public Administration, Associated Professor, Institute of Information Protection, State University of Telecommunications, Kyiv, Ukraine.
5Svitlana Lehominova, Doctor of Economics, Associated Professor, Institute of Information Protection, State University of Telecommunications, Kyiv, Ukraine.
6Volodymyr Chegrenets, PhD, Associated Professor, Institute of Information Protection, State University of Telecommunications, Kyiv, Ukraine.
Manuscript received on January 17, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 1803-1810 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1768029420/2020©BEIESP | DOI: 10.35940/ijitee.D1768.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The article deals with social networks parameters that describe individuals within network. The basic idea is based on an assumption that individuals with common characteristics are likely to communicate with each other. This is a kind of epidemical model with the chance of sending some information, as a functions of distance between source and potential destination. The main approach in the article is based on clustering of local characteristics of network. They characterize degree of interaction between the closest neighbors of current graph node. For the most networks if a node A is connected to node B and node B – to node C, then, there is a big chance of the fact, that node A is connected to node C (friends of our friends are also our friends). Depending on the graph structure, between two nodes there are often a few different paths. Distribution of nodes degree is a distribution with “long tail” and is modelled with degree distribution. It means, that in such networks, there are a lot of nodes, that has 1-3 neighbors, but a little of nodes, which has thousands of neighbors. A modelling of parameters (possible quantity of graph edges, clustering coefficient, connection of new node, shortest and average path, residual lifetime of chain, node interactions, average degree of node etc.) of social networks is taken. Calculations are illustrated with graphical materials. Relevant equations are represented.
Keywords: Social Network, Graph, Node Interaction, Information Security, Modelling.
Scope of the Article: Plant Cyber security