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Epidemic dynamics on temporal complex networks
Phys. Rev. E 114, 024306 – Published 11 August, 2026
DOI: https://doi.org/10.1103/48z3-6cmb
Abstract
A wide range of real-world systems, ranging from physical and biological systems to social systems, can be described as temporal networks, where link temporality has profound effects on many dynamical processes taking place on networks. Here we study the susceptible-infected-susceptible model on temporal complex networks, where edges are randomly activated or terminated at each time step. We analytically obtain the epidemic threshold for temporal complex networks with arbitrary initial degree distributions. The approximate endemic equilibrium prevalence for temporal complex networks with arbitrary initial degree distributions is derived analytically for the system close to the epidemic threshold and far away from it. In addition, we demonstrate that the system undergoes a continuous transition from the disease-free state to the endemic state. Our results pave the way to understand how the degree distributions will affect a wide variety of binary-state dynamics on temporal complex networks.
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References (69)
- A. Barrat, M. Barthélemy, and A. Vespignani, Dynamical Processes on Complex Networks (Cambridge University Press, Cambridge, 2008).
- M. Newman, Networks (Oxford University Press, Oxford, 2018).
- C. Castellano, S. Fortunato, and V. Loreto, Statistical physics of social dynamics, Rev. Mod. Phys. 81, 591 (2009).
- D. Li, W. Xie, D. Han, and M. Sun, A multi-information epidemic spreading model on a two-layer network, Inf. Sci. 651, 119723 (2023).
- W. Wang, Y. Nie, W. Li, T. Lin, M.-S. Shang, S. Su, Y. Tang, Y.-C. Zhang, and G.-Q. Sun, Epidemic spreading on higher-order networks, Phys. Rep. 1056, 1 (2024).
- Y. Wang, L. Tu, X. Wang, and Y. Guo, Evolutionary vaccination game considering intra-seasonal strategy shifts regarding multi-seasonal epidemic spreading, Chaos Solitons Fract. 180, 114419 (2024).
- J. Zhang, C. Yang, Z. Jin, and J. Li, Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks, J. Theor. Biol. 449, 1 (2018).
- R. C. Barnard, L. Berthouze, P. L. Simon, and I. Z. Kiss, Epidemic threshold in pairwise models for clustered networks: Closures and fast correlations, J. Math. Biol. 79, 823 (2019).
- Q. Yin, Z. Wang, C. Xia, and C. T. Bauch, Impact of co-evolution of negative vaccine-related information, vaccination behavior and epidemic spreading in multilayer networks, Commun. Nonlinear Sci. Numer. Simul. 109, 106312 (2022).
- J.-X. Yang, Z.-P. Cao, and Y. Lu, Contagion dynamics on a compound model, Appl. Math. Comput. 460, 128293 (2024).
- P. Holme and J. Saramäki, Temporal networks, Phys. Rep. 519, 97 (2012).
- N. Masuda, K. Klemm, and V. M. Eguíluz, Temporal networks: Slowing down diffusion by long lasting interactions, Phys. Rev. Lett. 111, 188701 (2013).
- J.-C. Delvenne, R. Lambiotte, and L. E. Rocha, Diffusion on networked systems is a question of time or structure, Nat. Commun. 6, 7366 (2015).
- I. Scholtes, N. Wider, R. Pfitzner, A. Garas, C. J. Tessone, and F. Schweitzer, Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, Nat. Commun. 5, 5024 (2014).
- T. Hiraoka and H.-H. Jo, Correlated bursts in temporal networks slow down spreading, Sci. Rep. 8, 15321 (2018).
- P. Van Mieghem and R. van de Bovenkamp, Non-Markovian infection spread dramatically alters the susceptible-infected-susceptible epidemic threshold in networks, Phys. Rev. Lett. 110, 108701 (2013).
- S. Cure, F. G. Pflug, and S. Pigolotti, Exponential rate of epidemic spreading on complex networks, Phys. Rev. E 111, 044311 (2025).
- A. Li, L. Zhou, Q. Su, S. P. Cornelius, Y.-Y. Liu, L. Wang, and S. A. Levin, Evolution of cooperation on temporal networks, Nat. Commun. 11, 2259 (2020).
- Y. Meng, A. McAvoy, and A. Li, Promoting collective cooperation through temporal interactions, Proc. Natl. Acad. Sci. USA 122, e2509575122 (2025).
- S. Nag Chowdhury, S. Kundu, M. Duh, M. Perc, and D. Ghosh, Cooperation on interdependent networks by means of migration and stochastic imitation, Entropy 22, 485 (2020).
- M. S. Anwar, S. Rakshit, D. Ghosh, and E. M. Bollt, Stability analysis of intralayer synchronization in time-varying multilayer networks with generic coupling functions, Phys. Rev. E 105, 024303 (2022).
- S. Rakshit, S. Majhi, and D. Ghosh, Stability analysis of synchronization in long-range temporal networks using theory of dichotomy, Chaos 34, 033114 (2024).
- S. N. Chowdhury and H. Meyer-Ortmanns, Metastability induced by non-reciprocal adaptive couplings in Kuramoto models, Front. Netw. Physiol. 6, 1774273 (2026).
- S. N. Chowdhury, S. Majhi, M. Ozer, D. Ghosh, and M. Perc, Synchronization to extreme events in moving agents, New J. Phys. 21, 073048 (2019).
- S. N. Chowdhury, S. Majhi, and D. Ghosh, Distance dependent competitive interactions in a frustrated network of mobile agents, IEEE Trans. Network Sci. Eng. 7, 3159 (2020).
- T. Onaga, J. P. Gleeson, and N. Masuda, Concurrency-induced transitions in epidemic dynamics on temporal networks, Phys. Rev. Lett. 119, 108301 (2017).
- Y. Long, X. Liu, and Z. Liu, Temporal stability of the dynamic resting-state functional brain network: Current measures, clinical research progress, and future perspectives, Brain Sci. 13, 429 (2023).
- G. Miritello, E. Moro, and R. Lara, Dynamical strength of social ties in information spreading, Phys. Rev. E 83, 045102(R) (2011).
- M. Karsai, M. Kivelä, R. K. Pan, K. Kaski, J. Kertész, A.-L. Barabási, and J. Saramäki, Small but slow world: How network topology and burstiness slow down spreading, Phys. Rev. E 83, 025102(R) (2011).
- L. Ferreri, P. Bajardi, M. Giacobini, S. Perazzo, and E. Venturino, Interplay of network dynamics and heterogeneity of ties on spreading dynamics, Phys. Rev. E 90, 012812 (2014).
- N. Perra, B. Gonçalves, R. Pastor-Satorras, and A. Vespignani, Activity driven modeling of time varying networks, Sci. Rep. 2, 469 (2012).
- E. Valdano, L. Ferreri, C. Poletto, and V. Colizza, Analytical computation of the epidemic threshold on temporal networks, Phys. Rev. X 5, 021005 (2015).
- C.-R. Cai, Y.-Y. Nie, and P. Holme, Epidemic criticality in temporal networks, Phys. Rev. Res. 6, L022017 (2024).
- Z. Luo, S. Li, Z. Jiang, and W. Chen, Social contagion models on adaptive simplicial complexes, Physica A 679, 131010 (2025).
- G. V. Clemente and D. Garlaschelli, Linking through time: Memory-enhanced community discovery in temporal networks, Phys. Rev. Res. 6, 043204 (2024).
- B. Chen, G. Hou, and A. Li, Temporal local clustering coefficient uncovers the hidden pattern in temporal networks, Phys. Rev. E 109, 064302 (2024).
- L. Zino and M. Cao, Analysis, prediction, and control of epidemics: A survey from scalar to dynamic network models, IEEE Circuits Syst. Mag. 21, 4 (2021).
- T. Shiraga and S. Kijima, An analysis of load-balancing algorithms on edge-Markovian evolving graphs, J. Comput. Syst. Sci. 160, 103797 (2026).
- M. Ogura and V. M. Preciado, Stability of spreading processes over time-varying large-scale networks, IEEE Trans. Network Sci. Eng. 3, 44 (2016).
- E. Volz and L. A. Meyers, Susceptible–infected–recovered epidemics in dynamic contact networks, Proc. R. Soc. B 274, 2925 (2007).
- E. Volz and L. A. Meyers, Epidemic thresholds in dynamic contact networks, J. R. Soc. Interface 6, 233 (2009).
- A. Szabó, P. L. Simon, and I. Z. Kiss, Detailed study of bifurcations in an epidemic model on a dynamic network, Differ. Equ. Appl. 4, 277 (2012).
- M. Taylor, T. J. Taylor, and I. Z. Kiss, Epidemic threshold and control in a dynamic network, Phys. Rev. E 85, 016103 (2012).
- A. Szabó-Solticzky, L. Berthouze, I. Z. Kiss, and P. L. Simon, Oscillating epidemics in a dynamic network model: Stochastic and mean-field analysis, J. Math. Biol. 72, 1153 (2016).
- C. Corcoran and J. M. Clark, Adaptive network modeling of social distancing interventions, J. Theor. Biol. 546, 111151 (2022).
- I. Tunc, M. S. Shkarayev, and L. B. Shaw, Epidemics in adaptive social networks with temporary link deactivation, J. Stat. Phys. 151, 355 (2013).
- M. S. Shkarayev, I. Tunc, and L. B. Shaw, Epidemics with temporary link deactivation in scale-free networks, J. Phys. A: Math. Theor. 47, 455006 (2014).
- T. Gross, C. J. Dommar D'Lima, and B. Blasius, Epidemic dynamics on an adaptive network, Phys. Rev. Lett. 96, 208701 (2006).
- F. L. Pinheiro, F. C. Santos, and J. M. Pacheco, Linking individual and collective behavior in adaptive social networks, Phys. Rev. Lett. 116, 128702 (2016).
- S. V. Scarpino, A. Allard, and L. Hébert-Dufresne, The effect of a prudent adaptive behaviour on disease transmission, Nat. Phys. 12, 1042 (2016).
- P. G. Sun, W. Che, Y. Quan, S. Wang, and Q. Miao, Random networks are heterogeneous exhibiting a multi-scaling law, Physica A 587, 126479 (2022).
- E. Ubaldi, N. Perra, M. Karsai, A. Vezzani, R. Burioni, and A. Vespignani, Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation, Sci. Rep. 6, 35724 (2016).
- N. Gozzi, M. Scudeler, D. Paolotti, A. Baronchelli, and N. Perra, Self-initiated behavioral change and disease resurgence on activity-driven networks, Phys. Rev. E 104, 014307 (2021).
- E. Valdano, M. R. Fiorentin, C. Poletto, and V. Colizza, Epidemic threshold in continuous-time evolving networks, Phys. Rev. Lett. 120, 068302 (2018).
- G. V. Clemente, C. J. Tessone, and D. Garlaschelli, Temporal networks with node-specific memory: Unbiased inference of transition probabilities, relaxation times, and structural breaks, Phys. Rev. Res. 6, 043257 (2024).
- M. J. Keeling, D. A. Rand, and A. J. Morris, Correlation models for childhood epidemics, Proc. R. Soc. Lond. B 264, 1149 (1997).
- M. J. Keeling and K. T. D. Eames, Networks and epidemic models, J. R. Soc. Interface 2, 295 (2005).
- P. L. Simon and I. Z. Kiss, Super compact pairwise model for SIS epidemic on heterogeneous networks, J. Complex Netw. 4, 187 (2016).
- T. House and M. J. Keeling, Insights from unifying modern approximations to infections on networks, J. R. Soc. Interface 8, 67 (2011).
- I. Z. Kiss, L. Berthouze, T. J. Taylor, and P. L. Simon, Modelling approaches for simple dynamic networks and applications to disease transmission models, Proc. R. Soc. A 468, 1332 (2012).
- M. Rao and P. Rao, Some more comments on “on the Routh-Hurwitz criterion”, IEEE Trans. Autom. Control 20, 714 (1975).
- C. Castillo-Chavez and B. Song, Dynamical models of tuberculosis and their applications, Math. Biosci. Eng. 1, 361 (2004).
- C. Corcoran and A. Hastings, A low-dimensional network model for an SIS epidemic: Analysis of the super compact pairwise model, Bull. Math. Biol. 83, 77 (2020).
- V. Sood and S. Redner, Voter model on heterogeneous graphs, Phys. Rev. Lett. 94, 178701 (2005).
- C. Castellano and R. Pastor-Satorras, Zero temperature Glauber dynamics on complex networks, J. Stat. Mech. (2006) P05001.
- S. N. Dorogovtsev, A. V. Goltsev, and J. F. F. Mendes, Ising model on networks with an arbitrary distribution of connections, Phys. Rev. E 66, 016104 (2002).
- J. Li, Z. Jin, and Y. Yuan, Effect of adaptive rewiring delay in a SIS network epidemic model, Math. Biosci. Eng. 16, 8092 (2019).
- I. Tunc and L. B. Shaw, Effects of community structure on epidemic spread in an adaptive network, Phys. Rev. E 90, 022801 (2014).
- Z. Luo, SIS model on temporal networks, https://github.com/ztluo123/epidemic-dynamics-on-temporal-complex-networks.