Proceedings of the 18th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2023F539

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1023–1027 (2023)

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Abstract. Complex networks attract attention in various scientific fields due to their ability to model real world phenomena and potential for problem-solving. It is essential to evaluate these networks to simulate and solve various issues. Evaluating social networks is challenging due to the unequal status of nodes and their unknown impact on everall characteristics. Existing measures of centrality often need to consider the global structure of the network, which requires the involvement of experts and creates space for multi-criteria decision-making methods usage. Unfortunately, more access to established decision-making models is often needed for various reasons. In this article, we propose an innovative approach called Dynamic SITCOM, which considers the preferences of characteristic objects and the characteristic values of criteria, enabling the re-identification of multi-criteria decision models. The approach evaluates nodes in Facebook's complex social network, focusing on prediction accuracy using similarity measures and mean absolute error. The study shows that a stable decision model can be created and applied to evaluate nodes in complex networks.

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