Efficient allocation of heterogeneous response times in information spreading process

Abstract

Recently, the impacts of spatiotemporal heterogeneities of human activities on spreading dynamics have attracted extensive attention. In this paper, we intend to understand how the heterogeneous distribution of response times at the individual level influences information spreading. Based on the uncorrelated scale-free networks without degree-degree correlation, we study the susceptible-infected spreading dynamics with adjustable power-law response time distribution, and find that the stronger the heterogeneity of response times is, the faster the information spreading is in the early and middle stages. Following a given heterogeneity, the procedure of reducing the correlation between the response times and degrees of individuals can also accelerate the spreading dynamics in the early and middle stages. However, the dynamics in the late stage is slightly more complicated, and there is an optimal value of the full prevalence time (i.e., the time for full infection on a network) changing with the heterogeneity of response times and the response time-degree correlation, respectively. The optimal phenomena result from the efficient allocation of heterogeneous response times.

Stability Analysis of SIR Model with Distributed Delay on Complex Networks

“…Model (2) offers a cognition of the propagation of epidemics, information and financial risk in complex systems. Since many real applications, such as infectious disease [ 19 ], information propagation [ 21 ], computer virus [ 26 ] and financial risks transmission [ 27 ] are all correlated with the epidemic dynamics on networks, more detailed justifications for epidemiology on networks have been carried out by some researchers, among which are J. Zhang et al [ 3 ] and Y. Wang et al [ 8 ], M. Small [ 18 , 19 ], M. Newman [ 28 ], A. Cui et al [ 29 ].…”

Unification of theoretical approaches for epidemic spreading on complex networks

“…Both Monte Carlo simulations [14][15][16][17] and theoretical study [18] have investigated the effects of network structures on epidemic spreading velocity [19,20], epidemic variability [21,22], epidemic size [23][24][25][26][27][28], and epidemic thresholds [29][30][31][32][33][34]. Both the epidemic size and threshold can indicate the probability of an epidemic occurring [32], which seeds are influential [35][36][37][38], and how to effectively control the epidemic once it begins [39][40][41].…”

Effective information spreading based on local information in correlated networks

“…Specially, for scale-free networks with degree exponent γ ≤ 3, the outbreak threshold vanishes in the thermodynamic limit [18,19,24,25]. Further studies revealed that the degree heterogeneity promotes spreading outbreaks, however limits the outbreak size at large transmission rates [13].Utilizing network information to effectively enhance the spreading speed and outbreak size is an important topic in spreading dynamics studies [26][27][28][29][30]. The studies on effective information spreading can provide inspiration for epidemic controlling [31][32][33], as well as marketing strategies optimization [34][35][36].…”

“…Utilizing network information to effectively enhance the spreading speed and outbreak size is an important topic in spreading dynamics studies [26][27][28][29][30]. The studies on effective information spreading can provide inspiration for epidemic controlling [31][32][33], as well as marketing strategies optimization [34][35][36].…”