Intervention against information diffusion in static and temporal coupling networks
Yun Chai(柴允)1, You-Guo Wang(王友国)2,†, Jun Yan(颜俊)1, and Xian-Li Sun(孙先莉)1
1 School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; 2 School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Abstract Information diffusion in complex networks has become quite an active research topic. As an important part of this field, intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers. In particular, it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks. For this purpose, we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks. First, individual interactions are described by a modified activity-driven network (ADN) model. Then, we establish a novel node-based susceptible-infected-recovered-susceptible (SIRS) model to characterize the information diffusion dynamics. On these bases, three synergetic intervention strategies are formulated. Second, we derive the critical threshold of the controlled-SIRS system via stability analysis. Accordingly, we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget. Third, we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense, in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm. Finally, extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 62071248).
Corresponding Authors:
You-Guo Wang
E-mail: wangyg@njupt.edu.cn
Cite this article:
Yun Chai(柴允), You-Guo Wang(王友国), Jun Yan(颜俊), and Xian-Li Sun(孙先莉) Intervention against information diffusion in static and temporal coupling networks 2023 Chin. Phys. B 32 090202
[1] Zhang Q, Zhong L, Gao S and Li X 2018 IEEE Trans. Cybern48 3411 [2] Ferrag M A, Shu L and Choo K K R 2021 IEEECAA Journal of Automatica Sinica8 1477 [3] Cheng S M, Chen P Y, Lin C C and Hsiao H C 2017 IEEE Commun. Mag.55 29 [4] Liu X, He D and Liu C 2019 IEEE Trans. Comput. Soc. Syst.6 8 [5] Hernández Guillén J D and Martín del Rey A 2018 Commun. Nonlinear Sci.Numer. Simul.56 217 [6] Zhu L and Wang B 2020 Inf. Sci.526 1 [7] Ruf S F, Paarporn K and Paré P E 2020 IEEE Trans. Netw. Sci. Eng.7 1764 [8] Preciado V M, Zargham M, Enyioha C, Jadbabaie A and Pappas G J 2014 IEEE Trans. Control Netw. Syst.1 99 [9] Nowzari C, Preciado V M and Pappas G J 2017 IEEE Trans. Control Netw. Syst.4 159 [10] Watkins N J, Nowzari C, Preciado V M and Pappas G J 2018 IEEE Trans. Control Netw. Syst.5 298 [11] Abad Torres J, Roy S and Wan Y 2017 IEEE Trans. Autom. Control62 1714 [12] Zhao T F, Chen W N, Liew A W C, Gu T, Wu X K and Zhang J 2021 IEEE Trans. Syst. Man Cybern. Syst.51 5090 [13] Zhao T F, Chen W N, Kwong S, Gu T L, Yuan H Q, Zhang J and Zhang J 2021 IEEE Trans. Cybern.51 3752 [14] Nowzari C, Preciado V M, and Pappas G J 2016 IEEE Control Syst. Mag.36 26 [15] Chen L and Sun J 2014 Physica A410 196 [16] Kandhway K and Kuri J 2017 IEEE Trans. Syst. Man Cybern. Syst.47 1099 [17] Zhao D, Wang L, Wang Z and Xiao G 2019 IEEE Trans. Inf. Forensics Secur.14 1755 [18] Wang X, Wang X, Hao F, Min G and Wang L 2019 IEEE Trans. Netw. Serv. Manag.16 1226 [19] Wang X, Wang X, Min G, Hao F and Chen C L P 2022 IEEE Trans. Cybern.52 87 [20] Lin Y, Wang X, Hao F, Jiang Y, Wu Y, Min G, He D, Zhu S and Zhao W 2021 IEEE Trans. Syst. Man Cybern. Syst.51 3725 [21] Yang L X, Draief M and Yang X 2016 Physica A450 403 [22] Hu P, Ding L and Hadzibeganovic T 2018 Commun. Nonlinear Sci. Numer. Simulat.63 339 [23] Liu F and Buss M 2020 IEEE Trans. Control Netw. Syst.7 1115 [24] Huo L and Chen X 2021 Chin. Phys. B30 120205 [25] Huo L and Dong Y 2022 Chin. Phys. B31 030202 [26] Zhang Y, Adiga A, Saha S, Vullikanti A and Prakash B A 2016 IEEE Trans. Knowl. Data Eng.28 3339 [27] Din A and Li Y 2022 Chin. Phys. B31 020202 [28] Tong G, Wu W, Guo L, Li D, Liu C, Liu B and Du D Z 2020 IEEE Trans. Netw. Sci. Eng.7 845 [29] Chen W N, Tan D Z, Yang Q, Gu T and Zhang J 2020 IEEE Trans. Cybern.50 4053 [30] Zino L, Rizzo A and Porfiri M 2018 SIAM J. Appl. Dyn. Syst.17 2830 [31] Perra N, Gonçalves B, Pastor-Satorras R and Vespignani A 2012 Sci. Rep.2 469 [32] Tizzani M, Lenti S, Ubaldi E, Vezzani A, Castellano C and Burioni R 2018 Phys. Rev. E98 062315 [33] Surano F V, Bongiorno C, Zino L, Porfiri M and Rizzo A 2019 Phys. Rev. E100 042306 [34] Nadini M, Bongiorno C, Rizzo A and Porfiri M 2020 Nonlinear Dyn.99 855 [35] Lei Y, Jiang X, Guo Q, Ma Y, Li M and Zheng Z 2016 Phys. Rev. E93 032308 [36] Nadini M, Rizzo A and Porfiri M 2020 IEEE Trans. Netw. Sci. Eng.7 549 [37] Peng S, Wang G, Zhou Y, Wan C, Wang C, Yu S and Niu J 2019 IEEE Trans. Dependable Secure Comput.16 984 [38] Li A, Cornelius S P, Liu Y Y, Wang L and Barabási A L 2017 Science358 1042 [39] Masuda N and Holme P 2017 Temporal Network Epidemiology, 1st edn. (Singapore: Springer) pp. 1-16 [40] Pare P E, Beck C L and Nedic A 2018 IEEE Trans. Control Netw. Syst.5 1322 [41] Zhan X X, Hanjalic A and Wang H 2019 Sci. Rep.9 6798 [42] Zhang Y Q, Li X and Vasilakos A V 2020 IEEE Trans. Cybern.50 1965 [43] Chen P Y, Cheng S M and Chen K C 2014 IEEE Trans. Cybern.44 2316 [44] Nowzari C, Ogura M, Preciado V M and Pappas G J November 8-11, 2015, Pacific Grove, USA, p. 1333 [45] Ogura M and Preciado V M 2017 IEEE Trans. Control Netw. Syst.4 213 [46] Li K, Zhang H, Zhu G, Small M and Fu X 2021 IEEE Trans. Syst. Man Cybern. Syst.51 2602 [47] Zino L, Rizzo A and Porfiri M 2020 Eur. J. Control54 1 [48] Zino L and Cao M 2021 IEEE Circuits Syst. Mag.21 4 [49] Ogura M, Preciado V M and Masuda N 2019 SIAM J. Appl. Math.79 986 [50] Zino L and Rizzo A 2020 IEEE Control Syst. Lett.4 797 [51] Van Mieghem P, Omic J and Kooij R 2009 IEEEACM Trans. Netw.17 1 [52] Zino L, Rizzo A and Porfiri M 2017 J. Complex Netw.5 924 [53] Perra N 2021 Phys. Rep.913 1 [54] Merkin D 1971 Introduction to the Theory of Stability (New York: Springer-Verlag) pp. 75-101 [55] Deng X, Long F, Li B, Cao D and Pan Y 2020 IEEE Trans. Netw. Sci. Eng.7 737 [56] Fleming W H and Rishel R W 1975 Deterministic and Stochastic Optimal Control (New York: Springer) pp. 20-79 [57] Eguíluz V M, Chialvo D R, Cecchi G A, Baliki M and Apkarian A V 2005 Phys. Rev. Lett.94 018102
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