TY - JOUR
T1 - Non-Markovian epidemic spreading on temporal networks
AU - Han, Lilei
AU - Lin, Zhaohua
AU - Yin, Qingqing
AU - Tang, Ming
AU - Guan, Shuguang
AU - Boguñá, Marián
N1 - This work was supported by the National ten thousand talents plan youth top talent project, China, the National Natural Science Foundation of China (Grant Nos. 11975099, 12231012, 11875132), the Science and Technology Commission of Shanghai Municipality, China (Grant No. 22DZ2229004) and the China Scholarship Council (Grant No. 202006140147). M. B. acknowledge support from: Grant TED2021- 129791B-I00 funded by MCIN/AEI/10.13039/501100011033 and the ‘‘European Union NextGenerationEU/PRTR’’; Grant PID2019-106290 GB-C22 funded by MCIN/AEI/10.13039/501100011033; Generalitat de Catalunya, Spain grant number 2021SGR00856 and the ICREA Academia award, funded by the Generalitat de Catalunya, Spain.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/8
Y1 - 2023/8
N2 - Many empirical studies have revealed that the occurrences of contacts associated with human activities are non-Markovian temporal processes with a heavy tailed inter-event time distribution. Besides, there has been increasing empirical evidence that the infection and recovery rates are time-dependent. However, we lack a comprehensive framework to analyze and understand non-Markovian contact and spreading processes on temporal networks. In this paper, we propose a general formalism to study non-Markovian dynamics on non-Markovian temporal networks. We find that, under certain conditions, non-Markovian dynamics on temporal networks are equivalent to Markovian dynamics on static networks. Interestingly, this result is independent of the underlying network topology.
AB - Many empirical studies have revealed that the occurrences of contacts associated with human activities are non-Markovian temporal processes with a heavy tailed inter-event time distribution. Besides, there has been increasing empirical evidence that the infection and recovery rates are time-dependent. However, we lack a comprehensive framework to analyze and understand non-Markovian contact and spreading processes on temporal networks. In this paper, we propose a general formalism to study non-Markovian dynamics on non-Markovian temporal networks. We find that, under certain conditions, non-Markovian dynamics on temporal networks are equivalent to Markovian dynamics on static networks. Interestingly, this result is independent of the underlying network topology.
KW - Equivalence
KW - Non-Markovian spreading dynamics
KW - Temporal networks
UR - https://www.scopus.com/pages/publications/85162849765
UR - https://www.sciencedirect.com/science/article/pii/S0960077923005659?via%3Dihub
U2 - 10.1016/j.chaos.2023.113664
DO - 10.1016/j.chaos.2023.113664
M3 - Journal article
AN - SCOPUS:85162849765
SN - 0960-0779
VL - 173
JO - Chaos, Solitons and Fractals
JF - Chaos, Solitons and Fractals
M1 - 113664
ER -
SDG 3 Good Health and Well-being