The Impact of Network Topology on Banking System Dynamics

  • Valentina Y. Guleva
  • Abdulmalik Amuda
  • Klavdiya Bochenina
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 674)


A topology of a banking network influences systemic stability under fixed banks’ and customers’ policy. Dynamical processes in a network affect topological changes at each iteration, so differences in initial topologies may result in different stability dynamics. Taking into account the influence of both nodes’ states dynamics and topology dynamics on the network fragility, the following ways of initial topology impact can be distinguished: (i) states of nodes after initialization (as a state of a node is influenced by its degree); (ii) nodes’ states dynamics; (iii) systemic risk at the fixed iteration. It seems that coevolution of network topology and states of nodes leads to the significant and unpredictable changes of initial conditions. We study the interrelations between initial and resulting system’s states for different types of initial topology. Our results confirm that the dynamics of a borrowing process is significantly influenced by topological features of the underlying interbank network.


Banking system Adaptive network Network topology Complex systems 



This work was partly performed by the Master student of the Masters Programme in Computational Science [3]. This paper is financially supported by The Russian Scientific Foundation, Agreement #14–21–00137 (15.08.2014).


  1. 1.
    Chung, F.R.K., Lu, L.: Complex graphs and networks. Am. Math. Soc. Providence 107 (2006). doi: 10.1090/cbms/107
  2. 2.
    Guleva, V.Y.: The combination of topology and nodes’ states dynamics as an early-warning signal of critical transition in a banking network model. Procedia Computer Science (2016). doi: 10.1016/j.procs.2016.05.436
  3. 3.
    Krzhizhanovskaya, V.V., et. al.: Russian-Dutch double-degree Masters programme in computational science in the age of global education. J. Comput. Sci. 10, pp. 288–298 (2015). doi: 10.1016/j.jocs.2015.05.001
  4. 4.
    May, R.M., Arinaminpathy, N.: Systemic risk: the dynamics of model banking systems. J. R. Soc. Interface 7(46), 823–838 (2010). doi: 10.1098/rsif.2009.0359
  5. 5.
    Van Dam, E.R., Haemers, W.H.: Which graphs are determined by their spectrum? Linear Algebra Appl. 373, 241–272 (2003). doi: 10.1016/S0024-3795(03)00483-X
  6. 6.
    Montagna, M., Kok, C.: Multi-layered interbank model for assessing systemic risk. Kiel Working Paper, no. 1873 (2013).
  7. 7.
    Purica, I.: Nonlinear Dynamics of Financial Crises: How to Predict Discontinuous Decisions. Academic Press, ISBN: 978-0-12-803275-6 (2015)Google Scholar
  8. 8.
    Ye, C., Torsello, A., Wilson, R.C., Hancock, E.R.: Thermodynamics of time evolving networks. In: Liu, C.-L., Luo, B., Kropatsch, W.G., Cheng, J. (eds.) GbRPR 2015. LNCS, vol. 9069, pp. 315–324. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-18224-7_31 Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Valentina Y. Guleva
    • 1
  • Abdulmalik Amuda
    • 1
  • Klavdiya Bochenina
    • 1
  1. 1.ITMO UniversitySaint PetersburgRussia

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