Guo-Ping Jiang

Epidemic spreading in metapopulation networks with heterogeneous infection rates

Abstract In this paper, we study epidemic spreading in metapopulation networks wherein each node represents a subpopulation symbolizing a city or an urban area and links connecting nodes correspond to the human traveling routes among cities. Differently from previous studies, we introduce a heterogeneous infection rate to characterize the effect of nodes’ local properties, such as population density, individual health habits, and social conditions, on epidemic infectivity. By means of a mean-field approach and Monte Carlo simulations, we explore how the heterogeneity of the infection rate affects the epidemic dynamics, and find that large fluctuations of the infection rate have a profound impact on the epidemic threshold as well as the temporal behavior of the prevalence above the epidemic threshold. This work can refine our understanding of epidemic spreading in metapopulation networks with the effect of nodes’ local properties.

Modeling and Analyzing the Interaction between Network Rumors and Authoritative Information

In this paper, we propose a novel two-stage rumor spreading Susceptible-Infected-Authoritative-Removed (SIAR) model for complex homogeneous and heterogeneous networks. The interaction Markov chains (IMC) mean-field equations based on the SIAR model are derived to describe the dynamic interaction between the rumors and authoritative information. We use a Monte Carlo simulation method to characterize the dynamics of the Susceptible-Infected-Removed (SIR) and SIAR models, showing that the SIAR model with consideration of authoritative information gives a more realistic description of propagation features of rumors than the SIR model. The simulation results demonstrate that the critical threshold λc of the SIAR model has the tiniest increase than the threshold of SIR model. The sooner the authoritative information is introduced, the less negative impact the rumors will bring. We also get the result that heterogeneous networks are more prone to the spreading of rumors. Additionally, the inhibition of rumor spreading, as one of the characteristics of the new SIAR model itself, is instructive for later studies on the rumor spreading models and the controlling strategies.

Time-varying human mobility patterns with metapopulation epidemic dynamics

Abstract In this paper, explicitly considering the influences of an epidemic outbreak on human travel, a time-varying human mobility pattern is introduced to model the time variation of global human travel. The impacts of the pattern on epidemic dynamics in heterogeneous metapopulation networks, wherein each node represents a subpopulation with any number of individuals, are investigated by using a mean-field approach. The results show that the pattern does not alter the epidemic threshold, but can slightly lower the final average density of infected individuals as a whole. More importantly, we also find that the pattern produces different impacts on nodes with different degree, and that there exists a critical degree k c . For nodes with degree smaller than k c , the pattern produces a positive impact on epidemic mitigation; conversely, for nodes with degree larger than k c , the pattern produces a negative impact on epidemic mitigation.

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

Defence against cascading failures is of great theoretical and practical significance. A novel load capacity model with a tunable proportion is proposed. We take degree and clustering coefficient into account to redistribute the loads of broken nodes. The redistribution is local, where the loads of broken nodes are allocated to their nearest neighbours. Our model has been applied on artificial networks as well as two real networks. Simulation results show that networks get more vulnerable and sensitive to intentional attacks along with the decrease of average degree. In addition, the critical threshold from collapse to intact states is affected by the tunable parameter. We can adjust the tunable parameter to get the optimal critical threshold and make the systems more robust against cascading failures.

Modeling and analysis of epidemic spreading on community networks with heterogeneity

Abstract A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold.

An Improved Weighted LeaderRank Algorithm for Identifying Influential Spreaders in Complex Networks

Identifying influential spreaders in social network is of great theoretical and practical significance. In this paper, we propose an improved weighted LeaderRank algorithm. Instead of considering degree of node only, we also take clustering coefficient into account to depict the weight. Moreover, we change the way of score assignment, which can give more scores to those high-influence nodes. Compared with PageRank and weighted LeaderRank, simulations show that not only can our algorithm make a quicker convergence but also select nodes which play a more significant role in the spreading process.

How clustering affects epidemics in complex networks

Epidemic spreading is closely related with topology of complex networks. Besides heterogeneity in the number of contacts, the clustering of contacts can also have far-reaching epidemiological consequences. Here we investigate the effects of clustering coefficient on epidemic dynamics in complex networks. Results show that the effects of clustering on epidemics differ in different networks. In homogeneous networks, the clustering can inhibit the epidemics when the infection scale is small. However, the inhibition effect will be reduced as the infection goes on. While in heterogeneous networks, there is no inhibition effect during all the process of the infection, which is also confirmed in real networks.