Abstract
This paper investigates the algebraic formulation and Nash equilibrium of competitive diffusion games by using semi-tensor product method, and gives some new results. Firstly, an algebraic formulation of competitive diffusion games is established via the semi-tensor product of matrices, based on which all the fixed points (the end of the diffusion process) are obtained. Secondly, using the algebraic formulation, a necessary and sufficient condition is presented for the verification of pure-strategy Nash equilibrium. Finally, an illustrative example is given to demonstrate the effectiveness of the obtained new results.


Similar content being viewed by others
Notes
The reason why we choose this kind of diffusion process is that it captures the simple fact that being closer to player’s initial seeds will result in adopting that specific player’s type.
References
Alon N, Feldman M, Procaccia AD, Tennenholtz M (2010) A note on competitive diffusion through social networks. Inf Process Lett 110:221–225
Bharathi S, Kempe D, Salek M (2007) Competitive influence maximization in social networks. In: Proceedings of international conference on internet and network economics, pp 306–311
Chen H, Sun J (2013) Global stability and stabilization of switched Boolean network with impulsive effects. Appl Math Comput 224:625–634
Cheng D, Qi H, Li Z (2011) Analysis and control of boolean networks: a semi-tensor product approach. Springer, London
Cheng D (2014) On finite potential games. Automatica 50(7):1793–1801
Cheng D, Xu T, Qi H (2014) Evolutionarily stable strategy of networked evolutionary games. IEEE Trans Neural Netw Learn Syst 25(7):1335–1345
Cheng D, He F, Qi H, Xu T (2015) Modeling, analysis and control of networked evolutionary games. IEEE Trans Autom Control 60(9):2402–2415
Etesami SR, Basar T (2016) Complexity of equilibrium in competitive diffusion games on social networks. Automatica 68:100–110
Fornasini E, Valcher ME (2013) Observability, reconstructibility and state observers of Boolean control networks. IEEE Trans Autom Control 58(6):1390–1401
Ghaderi J, Srikant R (2013) Opinion dynamics in social networks: a local interaction game with stubborn agents. In: Proccedings of 2013 American control conference, pp 1982–1987
Goyal S, Kearns M (2012) Competitive contagion in networks. In: Proceedings of the 44th symposium on theory of computing, pp 759–774
Guo P, Wang Y, Li H (2013) Algebraic formulation and strategy optimization for a class of evolutionary network games via semi-tensor product method. Automatica 49(11):3384–3389
Han M, Liu Y, Tu Y (2014) Controllability of Boolean control networks with time delays both in states and inputs. Neurocomputing 129:467–475
Jadbabaie A, Lin J, Morse AS (2003) Coordination of groups of mobile autonomous agents using nearest neighbor. IEEE Trans Autom Control 48(6):1675–1675
Khanafer A, Basar T (2014) Information spread in networks: control, games, and equilibria. In: 2014 information theory and applications workshop, pp 1–10
Laschov D, Margaliot M (2013) Minimum-time control of Boolean networks. SIAM J Control Optim 51(4):2869–2892
Li H, Wang Y (2015) Controllability analysis and control design for switched Boolean networks with state and input constraints. SIAM J Control Optim 53(5):2955–2979
Li H, Xie L, Wang Y (2016) On robust control invariance of Boolean control networks. Automatica 68:392–396
Liu X, Zhu J (2016) On potential equations of finite games. Automatica 68:245–253
Liu Y, Chen H, Lu J, Wu B (2015) Controllability of probabilistic Boolean control networks based on transition probability matrices. Automatica 52:340–345
Li H, Wang Y, Xie L (2015) Output tracking control of Boolean control networks via state feedback: constant reference signal case. Automatica 59:54–59
Lu J, Li H, Liu Y, Li F (2017) A survey on semi-tensor product method with its applications in logical networks and other finite-valued systems. IET Control Theory Appl. doi:10.1049/iet-cta.2016.1659
Meng M, Feng J (2014) Topological structure and the disturbance decoupling problem of singular Boolean networks. IET Control Theory Appl 8(13):1247–1255
Tang Y, Wang Z, Fang J (2009) Pinning control of fractional-order weighted complex networks. Chaos 19(1):193–204
Wang X, Chen G, Ching W (2002) Pinning control of scale-free dynamical networks. Phys A 310(3–4):521–531
Xu X, Hong Y (2013) Matrix approach to model matching of asynchronous sequential machines. IEEE Trans Autom Control 58(11):2974–2979
Yang M, Li R, Chu T (2013) Controller design for disturbance decoupling of Boolean control networks. Automatica 49(1):273–277
Young HP (2000) The diffusion of innovations in social networks. Gen Inf 413(1):2329–2334
Yu W, Chen G, Lv J (2009) On pinning synchronization of complex dynamical networks. Automatica 45:429–435
Zhang J, Huang Z, Dong J, Huang L, Lai Y (2013) Controlling collective dynamics in complex minority-game resource-allocation systems. Phys Rev E 87:052808
Zhang L, Zhang K (2013) Controllability and observability of Boolean control networks with time-variant delays in states. IEEE Trans Neural Netw Learn Syst 24:1478–1484
Zhao Y, Li Z, Cheng D (2011) Optimal control of logical control networks. IEEE Trans Autom Control 56(8):1766–1776
Zhong J, Lu J, Liu Y, Cao J (2014) Synchronization in an array of output-coupled Boolean networks with time delay. IEEE Trans Neural Netw Learn Syst 25:2288–2294
Zhu B, Xia X, Wu Z (2016) Evolutionary game theoretic demand-side management and control for a class of networked smart grid. Automatica 70:94–100
Zou Y, Zhu J (2015) Kalman decomposition for Boolean control networks. Automatica 54:65–71
Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions which improved the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
The research was supported by the National Natural Science Foundation of China under Grants 61374065 and 61503225, the Natural Science Foundation of Shandong Province under Grant ZR2015FQ003, and the Natural Science Fund for Distinguished Young Scholars of Shandong Province under Grant JQ201613.
Rights and permissions
About this article
Cite this article
Li, H., Ding, X., Yang, Q. et al. Algebraic Formulation and Nash Equilibrium of Competitive Diffusion Games. Dyn Games Appl 8, 423–433 (2018). https://doi.org/10.1007/s13235-017-0228-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13235-017-0228-4