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Dynamic SITCOM: an innovative approach to re-identify social network evaluation models

Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
Strony
1023--1027
Opis fizyczny
Bibliogr. 22 poz., tab., il.
Twórcy
  • Dept. of Artificial Intelligence Methods and Applied Mathematics, West Pomeranian University of Technology in Szczecin ul. Żołnierska 49, 71-210 Szczecin, Poland
  • Dept. of Information Systems Engineering, West Pomeranian University of Technology in Szczecin ul. Żołnierska 49, 71-210 Szczecin, Poland
Bibliografia
  • 1. Z. Liu, C. Jiang, J. Wang, and H. Yu, “The node importance in actual complex networks based on a multi-attribute ranking method,” Knowledge-Based Systems, vol. 84, pp. 56–66, 2015.
  • 2. Y. Yang, L. Yu, Z. Zhou, Y. Chen, T. Kou et al., “Node importance ranking in complex networks based on multicriteria decision making,” Mathematical Problems in Engineering, vol. 2019, 2019.
  • 3. L. Wang, Z. Yu, F. Xiong, D. Yang, S. Pan, and Z. Yan, “Influence spread in geo-social networks: a multiobjective optimization perspective,” IEEE Transactions on Cybernetics, vol. 51, no. 5, pp. 2663–2675, 2019.
  • 4. A. Zareie, A. Sheikhahmadi, and K. Khamforoosh, “Influence maximization in social networks based on TOPSIS,” Expert Systems with Applications, vol. 108, pp. 96–107, 2018.
  • 5. M. Zhang, T. Huang, Z. Guo, and Z. He, “Complex-network-based traffic network analysis and dynamics: A comprehensive review,” Physica A: Statistical Mechanics and its Applications, p. 128063, 2022.
  • 6. J. Zhao, T.-H. Yang, Y. Huang, and P. Holme, “Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach,” PloS one, vol. 6, no. 9, p. e24306, 2011.
  • 7. M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse, “Identification of influential spreaders in complex networks,” Nature physics, vol. 6, no. 11, pp. 888–893, 2010.
  • 8. P. Yang, X. Liu, and G. Xu, “A dynamic weighted TOPSIS method for identifying influential nodes in complex networks,” Modern Physics Letters B, vol. 32, no. 19, p. 1850216, 2018.
  • 9. J. Zhang, Q. Zhang, L. Wu, and J. Zhang, “Identifying influential nodes in complex networks based on multiple local attributes and information entropy,” Entropy, vol. 24, no. 2, p. 293, 2022.
  • 10. Y. Du, C. Gao, Y. Hu, S. Mahadevan, and Y. Deng, “A new method of identifying influential nodes in complex networks based on TOPSIS,” Physica A: Statistical Mechanics and its Applications, vol. 399, pp. 57–69, 2014.
  • 11. B. Kizielewicz, “Towards the identification of continuous decisional model: the accuracy testing in the SITCOM approach,” Procedia Computer Science, vol. 207, pp. 4390–4400, 2022.
  • 12. B. Kizielewicz and W. Sałabun, “A new approach to identifying a multi-criteria decision model based on stochastic optimization techniques,” Symmetry, vol. 12, no. 9, p. 1551, 2020.
  • 13. A. Khaoula, M. MACHKOUR, and J. ANTARI, “Unsupervised Learning-based New Seed-Expanding Approach using Influential Nodes for Community Detection in Social Networks,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 1, 2023.
  • 14. Y. Zhang and S. T. Ng, “Identification and quantification of node criticality through EWM–TOPSIS: a study of Hong Kong’s MTR system,” Urban Rail Transit, vol. 7, no. 3, pp. 226–239, 2021.
  • 15. M. Lu, “Node importance evaluation based on neighborhood structure hole and improved TOPSIS,” Computer Networks, vol. 178, p. 107336, 2020.
  • 16. Y. Meng, X. Tian, Z. Li, W. Zhou, Z. Zhou, and M. Zhong, “Exploring node importance evolution of weighted complex networks in urban rail transit,” Physica A: Statistical Mechanics and its Applications, vol. 558, p. 124925, 2020.
  • 17. X. Mi, C. Shao, C. Dong, C. Zhuge, and Y. Zheng, “A framework for intersection traffic safety screening with the implementation of complex network theory,” Journal of advanced transportation, vol. 2020, pp. 1–12, 2020.
  • 18. S. G. Kharanagh, M. E. Banihabib, and S. Javadi, “An MCDM-based social network analysis of water governance to determine actors’ power in water-food-energy nexus,” Journal of Hydrology, vol. 581, p. 124382, 2020.
  • 19. Z. Lin, F. Wen, H. Wang, G. Lin, T. Mo, and X. Ye, “CRITIC-based node importance evaluation in skeleton-network reconfiguration of power grids,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 2, pp. 206–210, 2017.
  • 20. J. Leskovec and J. Mcauley, “Learning to discover social circles in ego networks,” Advances in neural information processing systems, vol. 25, 2012.
  • 21. N. V. Thieu and S. Mirjalili, “MEALPY: a Framework of The State-of-The-Art Meta-Heuristic Algorithms in Python,” Jun. 2022. [Online]. Available: https://doi.org/10.5281/zenodo.6684223
  • 22. B. Kizielewicz, A. Shekhovtsov, and W. Sałabun, “pymcdm—The universal library for solving multi-criteria decision-making problems,” SoftwareX, vol. 22, p. 101368, 2023.
Uwagi
1. Thematic Tracks Short Papers
2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f9e560fb-23dd-4ca8-b665-ca1946251374
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