Abstract
In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.
| Original language | English (US) |
|---|---|
| Article number | 062817 |
| Journal | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics |
| Volume | 91 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 29 2015 |
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ASJC Scopus subject areas
- Condensed Matter Physics
- Statistical and Nonlinear Physics
- Statistics and Probability
Cite this
Traffic-driven epidemic spreading in correlated networks. / Yang, Han Xin; Tang, Ming; Lai, Ying-Cheng.
In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 91, No. 6, 062817, 29.06.2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Traffic-driven epidemic spreading in correlated networks
AU - Yang, Han Xin
AU - Tang, Ming
AU - Lai, Ying-Cheng
PY - 2015/6/29
Y1 - 2015/6/29
N2 - In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.
AB - In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.
UR - http://www.scopus.com/inward/record.url?scp=84936972903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936972903&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.91.062817
DO - 10.1103/PhysRevE.91.062817
M3 - Article
AN - SCOPUS:84936972903
VL - 91
JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
SN - 1539-3755
IS - 6
M1 - 062817
ER -