Method for determining contagion dynamics on a multilayer network

1. A system for predicting temporal evolution of contagions on multilayer networks, the system comprising:

one or more processors and a non-transitory memory having instructions encoded thereon such that when the instructions are executed, the one or more processors perform operations of:

determining a functional epidemic threshold for disappearance of a contagion on a multilayer network model having a first layer and a second layer, comprised of a plurality of nodes, according to a score value s=λβ/δ, where λ corresponds to an adjacency matrix of the first layer of the multilayer network model, β represents a spread rate of the contagion, and δ represents a recovery rate;

outputting a prediction of future behavior of the contagion on the multilayer network model using the functional epidemic threshold; and

initiating a connectivity protocol based on the prediction of future behavior of the contagion,

wherein the connectivity protocol makes automated recommendations for preventing spread of the contagion.

2. The system as set forth in claim 1 , wherein the one or more processors further perform an operation of determining a boundary of a region in a (s,m) parameter space where the contagion disappears with a high probability, where m denotes an average number of edges per node in the multilayer network.

3. The system as set forth in claim 2 , wherein the boundary of the region is described by a hyperbolic function m=f(s).

4. The system as set forth in claim 1 , wherein the connectivity protocol makes automated recommendations for at least one of isolating an infected node in the multilayer network and removing edges in the multilayer network.

5. The system as set forth in claim 1 , wherein the one or more processors further perform operations of:

utilizing the functional epidemic threshold to assess robustness of an existing multilayer network; and

altering the existing multilayer network to make it more robust to contagion epidemics.

6. A computer-implemented method for predicting temporal evolution of contagions on multilayer networks, comprising:

an act of causing one or more processors to execute instructions stored on a non-transitory memory such that upon execution, the one or more processors perform operations of:

determining a functional epidemic threshold for disappearance of a contagion on a multilayer network model having a first layer and a second layer, comprised of a plurality of nodes, according to a score value s=λβ/δ, where λ corresponds to an adjacency matrix of the first layer of the multilayer network model, β represents a spread rate of the contagion, and δ represents a recovery rate;

outputting a prediction of future behavior of the contagion on the multilayer network model using the functional epidemic threshold; and

initiating a connectivity protocol based on the prediction of future behavior of the contagion,

wherein the connectivity protocol makes automated recommendations for preventing spread of the contagion.

7. The method as set forth in claim 6 , wherein the one or more processors further perform an operation of determining a boundary of a region in a (s,m) parameter space where the contagion disappears with a high probability, where m denotes an average number of edges per node in the multilayer network.

8. The method as set forth in claim 7 , wherein the boundary of the region is described by a hyperbolic function m=f(s).

9. The method as set forth in claim 6 , wherein the connectivity protocol makes automated recommendations for at least one of isolating an infected node in the multilayer network and removing edges in the multilayer network.

10. The method as set forth in claim 6 , wherein the one or more processors further perform operations of:

utilizing the functional epidemic threshold to assess robustness of an existing multilayer network; and

altering the existing multilayer network to make it more robust to contagion epidemics.

11. A computer program product for predicting temporal evolution of contagions on multilayer networks, the computer program product comprising:

computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:

determining a functional epidemic threshold for disappearance of a contagion on a multilayer network model having a first layer and a second layer, comprised of a plurality of nodes, according to a score value s=λβ/δ, where λ corresponds to an adjacency matrix of the first layer of the multilayer network model, β represents a spread rate of the contagion, and δ represents a recovery rate;

outputting a prediction of future behavior of the contagion on the multilayer network model using the functional epidemic threshold; and

initiating a connectivity protocol based on the prediction of future behavior of the contagion,

wherein the connectivity protocol makes automated recommendations for preventing spread of the contagion.

12. The computer program as set forth in claim 11 , further comprising instructions for causing the one or more processors to further perform an operation of determining a boundary of a region in a (s,m) parameter space where the contagion disappears with a high probability, where m denotes an average number of edges per node in the multilayer network.

13. The computer program product as set forth in claim 12 , wherein the boundary of the region is described by a hyperbolic function m=f(s).

14. The computer program as set forth in claim 11 , wherein the connectivity protocol makes automated recommendations for at least one of isolating an infected node in the multilayer network and removing edges in the multilayer network.

15. The computer program as set forth in claim 11 , further comprising instructions for causing the one or more processors to further perform operations of:

utilizing the functional epidemic threshold to assess robustness of an existing multilayer network; and

altering the existing multilayer network to make it more robust to contagion epidemics.