Abstract
Making an incorrect determination or ignoring a factor or interaction in a real-world socioeconomic system can greatly affect the functioning of the entire system, which in turn can lead to misconceptions and incorrect managerial decisions. Considering graph models of socioeconomic systems as the research object, where deterministic causality property is the fundamental characteristic of a graph edge, this study addresses the problem of influence control in models represented by directed weighted signed graphs with deterministic causality on edges. Influence control is considered from the point of view of the choice of influential nodes as points of application of control impacts, providing the possibility of targeted control in real-world socioeconomic systems. The algorithm of influence controls (AIC) is proposed as a tool to identify optimal control impacts. The algorithm maximizes the influence under the control model and uses a system of nonlinear constraints to design conditions for adequate model operation. The contributions made by this study are as follows: (1) the AIC validates the graph representation of the system under study; (2) by using AIC, new knowledge is discovered about important factors (i.e., target, or output) and influencing factors (i.e., impact objects, or input); (3) the appropriate metrics allow for the assessment of the compliance of this result with the degree of codirectionality of the response vector and the basic directionality vector of the system; and (4) the algorithm imposes no restrictions on the direction, sign or range of weights on the edges.
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References
Aguiar, M., & Bar-Yam, Y. (2005). Spectral analysis and the dynamic response of complex networks. Physical Review E, 71(1), 016106.
Albus, J. (2008). Toward a computational theory of mind. Journal of Mind Theory, 1(1), 1–38.
Alipour, M., Hafezi, R., Amer, M., & Akhavan, A. (2017). A new hybrid fuzzy cognitive map-based scenario planning approach for Iran’s oil production pathways in the post-sanction period. Energy, 135, 851–864.
Al-Nabki, M., Fidalgo, E., Alegre, E., & Fernández-Robles, L. (2019). ToRank: Identifying the most influential suspicious domains in the Tor network. Expert Systems with Applications, 123, 212–226.
Alsuwaidan, L., & Ykhlef, M. (2017). Information diffusion predictive model using radiation transfer. IEEE Access, 5, 25946–25957.
Arruda, G., Rodrigues, F., & Moreno, Y. (2018). Fundamentals of spreading processes in single and multilayer complex networks. Physics Reports, 756, 1–59.
Asadzadeh, S., Azadeh, A., Negahban, A., & Sotoudeh, A. (2013). Assessment and improvement of integrated HSE and macro-ergonomics factors by fuzzy cognitive maps: The case of a large gas refinery. Journal of Loss Prevention in the Process Industries, 26(6), 1015–1026.
Azevedo, A., & Ferreira, F. (2019). Analyzing the dynamics behind ethical banking practices using fuzzy cognitive mapping. Operational Research, 19(3), 679–700.
Barroso, R., Ferreira, F., Meidutė-Kavaliauskienė, I., Banaitienė, N., Falcão, F., & Rosa, A. (2019). Analyzing the determinants of e-commerce in small and medium-sized enterprises: A cognition-driven framework. Technological and Economic Development of Economy, 25(3), 496–518.
Bertsekas, D. (1982). Constrained optimization and lagrange multiplier methods. Massachusetts: MIT.
Butterworth, J., & Dunne, P. (2016). Spectral techniques in argumentation framework analysis. In P. Baroni, T. Gordon, T. Scheffler, & M. Stede (Eds.), Computational models of argument (pp. 167–178). Amsterdam: IOS Press Ebooks.
Carlucci, D., Ferreira, F., Schiuma, G., Jalali, M., & António, N. (2018). A holistic conception of sustainable banking: Adding value with fuzzy cognitive mapping. Technological and Economic Development of Economy, 24(4), 1303–1322.
Castellano, C., & Pastor-Satorras, R. (2012). Competing activation mechanisms in epidemics on networks. Scientific Reports, 2(1), 371.
Chang, B., Xu, T., Liu, Q., & Chen, E. (2018). Study on information diffusion analysis in social networks and its applications. International Journal of Automation and Computing, 15(4), 377–401.
Chen, G. (2017). Pinning control and controllability of complex dynamical networks. International Journal of Automation and Computing, 14(1), 1–9.
Dickison, M., Havlin, S., & Stanley, H. (2012). Epidemics on interconnected networks. Physical Review E, 85(6), 066109.
Dorogovtsev, S., Goltsev, A., & Mendes, J. (2002). Ising model on networks with an arbitrary distribution of connections. Physical Review E, 66(1), 016104.
Estrada, E. (2007). Topological structural classes of complex networks. Physical Review E, 75(1), 016103.
Fei, L., Zhang, Q., & Deng, Y. (2018). Identifying influential nodes in complex networks based on the inverse-square law. Physica A: Statistical Mechanics and its Applications, 512, 1044–1059.
Ferreira, F., Jalali, M., & Ferreira, J. (2016). Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables. Journal of Business Research, 69(4), 1471–1478.
Ferreira, F., & Meidutė-Kavaliauskienė, I. (2019). Toward a sustainable supply chain for social credit: Learning by experience using single-valued neutrosophic sets and fuzzy cognitive maps. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03194-2.
Gadiyaram, V., Ghosh, S., & Vishveshwara, S. (2016). A graph spectral-based scoring scheme for network comparison. Journal of Complex Networks, 5(2), 219–244.
Ghanbarnejad, F., & Klemm, K. (2012). Impact of individual nodes in Boolean network dynamics. EPL (Europhysics Letters), 99(5), 58006.
Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3), 211–223.
Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., & Schölkopf, B. (2016). Influence estimation and maximization in continuous-time diffusion networks. ACM Transactions on Information Systems, 34(2), 1–33.
Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.
Guo, L., Zhang, D., Cong, G., Wu, W., & Tan, K. (2017). Influence maximization in trajectory databases. IEEE Transactions on Knowledge and Data Engineering, 29(3), 627–641.
Harris, T. (1974). Contact interactions on a lattice. The Annals of Probability, 2(6), 969–988.
Helbing, D., & Kühnert, C. (2003). Assessing interaction networks with applications to catastrophe dynamics and disaster management. Physica A: Statistical Mechanics and its Applications, 328(3/4), 584–606.
Hobbs, B., Ludsin, S., Knight, R., Ryan, P., Biberhofer, J., & Ciborowski, J. (2002). Fuzzy cognitive mapping as a tool to define management objectives for complex ecosystems. Ecological Applications, 12(5), 1548–1565.
Horn, R., & Johnson, C. (2013). Matrix analysis. New York: Cambridge University Press.
Hu, Y., Wang, S., Ren, Y., & Choo, K. (2018). User influence analysis for Github developer social networks. Expert Systems with Applications, 108, 108–118.
Huang, H., Shen, H., & Meng, Z. (2019). Item diversified recommendation based on influence diffusion. Information Processing and Management, 56(3), 939–954.
Jastrzębska, A., & Cisłak, A. (2019). Interpretation-aware cognitive map construction for time series modeling. Fuzzy Sets and Systems, 361, 33–55.
Jun-Lan, X., Shu-Bin, S., Dong-Li, D., Chang-Chun, L., & Fei-Fei, X. (2019). Identification of influencers in networks with dynamic behaviors. Physica A: Statistical Mechanics and its Applications, 527, 121318.
Kabir, K., Kuga, K., & Tanimoto, J. (2019). Analysis of SIR epidemic model with information spreading of awareness. Chaos, Solitons & Fractals, 119, 118–125.
Kang, C., Kraus, S., Molinaro, C., Spezzano, F., & Subrahmanian, V. (2016). Diffusion centrality: A paradigm to maximize spread in social networks. Artificial Intelligence, 239, 70–96.
Kempe, D., Kleinberg, J., & Tardos, E. (2015). Maximizing the spread of influence through a social network. Theory of Computing, 11(1), 105–147.
Kim, J., Han, M., Lee, Y., & Park, Y. (2016). Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Systems with Applications, 57, 311–323.
Klemm, K., Serrano, M., Eguíluz, V., & Miguel, M. (2012). A measure of individual role in collective dynamics. Scientific Reports, 2(1), 292.
Knight, C., Lloyd, D., & Penn, A. (2014). Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points. Applied Soft Computing, 15, 193–202.
Kok, K. (2009). The potential of fuzzy cognitive maps for semi-quantitative scenario development, with an example from Brazil. Global Environmental Change, 19(1), 122–133.
Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.
Ladeira, M., Ferreira, F., Ferreira, J., Fang, W., Falcão, P., & Rosa, A. (2019). Exploring the determinants of digital entrepreneurship using fuzzy cognitive maps. International Entrepreneurship and Management Journal, 15(4), 1077–1110.
Lancaster, P., & Tismenetsky, M. (1985). The theory of matrices. Orlando: Academic Press.
Liu, J., Lin, J., Guo, Q., & Zhou, T. (2016). Locating influential nodes via dynamics-sensitive centrality. Scientific Reports, 6(1), 21380.
Liu, Y., Slotine, J., & Barabási, A. (2011). Controllability of complex networks. Nature, 473(7346), 167–173.
Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6), 1150–1170.
Mendon, M., Chrun, I., Arruda, L., & Papageorgiou, E. (2013). Autonomous navigation applying dynamic-fuzzy cognitive maps and fuzzy logic. In H. Papadopoulos, A. Andreou, L. Iliadis, & I. Maglogiannis (Eds.), Artificial intelligence applications and innovations (pp. 215–224). Berlin: Springer.
Mendonça, M., Angelico, B., Arruda, L., & Neves, F. (2013). A dynamic fuzzy cognitive map applied to chemical process supervision. Engineering Applications of Artificial Intelligence, 26(4), 1199–1210.
Mendonça, M., Chrun, I., Neves, F., & Arruda, L. (2017). A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps. Engineering Applications of Artificial Intelligence, 59, 122–132.
Mendonça, M., Silva, E., Chrun, I., Arruda, L. (2016). Hybrid dynamic fuzzy cognitive maps and hierarchical fuzzy logic controllers for autonomous mobile navigation, In Proceedings of the 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 2516–2521), 24–29 July 2016, IEEE, Vancouver, BC, Canada.
Mezei, J., & Sarlin, P. (2016). Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems, 88, 38–50.
Miguel, B., Ferreira, F., Banaitis, A., Banaitienė, N., Meidutė-Kavaliauskienė, I., & Falcão, P. (2019). An expanded conceptualization of “smart” cities: Adding value with fuzzy cognitive maps. Economics and Management, 22(1), 4–21.
Moreira, C., & de Aguiar, M. (2019). Global synchronization of partially forced Kuramoto oscillators on networks. Physica A: Statistical Mechanics and its Applications, 514, 487–496.
Okaniwa, M., & Ishii, H. (2012). An averaging method for synchronization in Kuramoto models. IFAC Proceedings Volumes, 45(26), 282–287.
Pandey, B., Bhanodia, P., Khamparia, A., & Pandey, D. (2019). A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges. Expert Systems with Applications, 124, 164–181.
Papageorgiou, E., & Salmeron, J. (2013). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1), 66–79.
Pei, S., Wang, J., Morone, F., & Makse, H. (2019). Influencer identification in dynamical complex systems. Journal of Complex Networks. https://doi.org/10.1093/comnet/cnz029.
Pires, A., Ferreira, F., Jalali, M., & Chang, H. (2018). Barriers to real estate investments for residential rental purposes: Mapping out the problem. International Journal of Strategic Property Management, 22(3), 168–178.
Poczeta, K., Kubuś, Ł., & Yastrebov, A. (2019). Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts. Biosystems, 179, 39–47.
Ribeiro, M., Ferreira, F., Jalali, M., & Meidutė-Kavaliauskienė, I. (2017). A fuzzy knowledge-based framework for risk assessment of residential real estate investments. Technological and Economic Development of Economy, 23(1), 140–156.
Salmeron, J. (2012). Fuzzy cognitive maps for artificial emotions forecasting. Applied Soft Computing, 12(12), 3704–3710.
Salmeron, J., Mansouri, T., Moghadam, M., & Mardani, A. (2019). Learning fuzzy cognitive maps with modified asexual reproduction optimisation algorithm. Knowledge-Based Systems, 163, 723–735.
Santos, F., Ferreira, F., & Meidutė-Kavaliauskienė, I. (2018). Perceived key determinants of payment instrument usage: A fuzzy cognitive mapping-based approach. Technological and Economic Development of Economy, 24(3), 950–968.
Shafia, M., Moghaddam, M., & Teimoury, E. (2016). Ranking fuzzy cognitive map based scenarios using ELECTRE III: Applied on housing market. Expert Systems, 33(5), 417–431.
Singh, S., Singh, K., Kumar, A., & Biswas, B. (2019). MIM2: Multiple influence maximization across multiple social networks. Physica A: Statistical Mechanics and its Applications, 526, 120902.
Tang, J., Zhang, R., Yao, Y., Zhao, Z., Wang, P., Li, H., et al. (2018). Maximizing the spread of influence via the collective intelligence of discrete bat algorithm. Knowledge-Based Systems, 160, 88–103.
Tikhonov, A., & Arsenin, V. (1977). Solutions of Ill-posed problems. New York: Wiley.
Tselykh, A., Vasilev, V., & Tselykh, L. (2019). Management of control impacts based on maximizing the spread of influence. International Journal of Automation and Computing, 16(3), 341–353.
Tselykh, A., Vasilev, V., Tselykh, L., & Barkovskii, S. (2017). Method maximizing the spread of influence in directed signed weighted graphs. Advances in Electrical and Electronic Engineering, 15(2), 203–214.
Wang, L., Liu, Q., Dong, S., & Soares, C. (2019). Effectiveness assessment of ship navigation safety countermeasures using fuzzy cognitive maps. Safety Science, 117, 352–364.
Wang, L., Su, R., Huang, Z., Wang, X., Wang, W., Grebogi, C., et al. (2016). A geometrical approach to control and controllability of nonlinear dynamical networks. Nature Communications, 7(1), 11323.
Wu, S., Sun, X., Li, X., & Wang, H. (2020). On controllability and observability of impulsive control systems with delayed impulses. Mathematics and Computers in Simulation, 171, 65–78.
Xavier, M., Ferreira, F., & Esperança, J. (2018). An intuition-based evaluation framework for social credit applications. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2995-8.
Zander, B., Liśkiewicz, M., & Textor, J. (2019). Separators and adjustment sets in causal graphs: Complete criteria and an algorithmic framework. Artificial Intelligence, 270, 1–40.
Zhang, R., Wang, X., Cheng, M., & Jia, T. (2019a). The evolution of network controllability in growing networks. Physica A: Statistical Mechanics and its Applications, 520, 257–266.
Zhang, B., Zhang, L., Mu, C., Zhao, Q., Song, Q., & Hong, X. (2019b). A most influential node group discovery method for influence maximization in social networks: A trust-based perspective. Data & Knowledge Engineering, 121, 71–87.
Zhao, S., & Sun, J. (2010). Controllability and observability for impulsive systems in complex fields. Nonlinear Analysis: Real World Applications, 11(3), 1513–1521.
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This work was supported by the Southern Federal University.
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Tselykh, A., Vasilev, V., Tselykh, L. et al. Influence control method on directed weighted signed graphs with deterministic causality. Ann Oper Res 311, 1281–1305 (2022). https://doi.org/10.1007/s10479-020-03587-8
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DOI: https://doi.org/10.1007/s10479-020-03587-8