Spreading of Failures in Interdependent Networks

Shekhtman, Louis; Danziger, Michael M.; Havlin, Shlomo

doi:10.1007/978-3-319-67798-9_20

Cited by 5 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, for the proper functioning of an interconnected system, insight into fault propagation mechanisms in an interconnected system is very important. This kind of research also provides insight into the robustness of the system (Shekhtman et al, 2023). Similarly, research has been done to revive a failed network through microscopic interventions.…”

Section: Introductionmentioning

confidence: 99%

SIR Epidemics in Interconnected Networks: threshold curve and phase transition

Das,

Scoglio

2023

Preprint

View full text Add to dashboard Cite

For simplicity of mathematical modelling of epidemic spreading, assumption is that hosts have identical rate of disease-causing contacts. However, in real world the scenario is different. The network-based framework allows us to capture the complex interdependencies and structural heterogeneity present in real-world systems. We examine two distinct scenarios involving the dynamics of Susceptible-Infected-Recovered (SIR) in interconnected networks. In the first part, we show how the epidemic threshold of a contact network changes as a result of being coupled with another network for a fixed infection strength. The model employed in this work considers both the contact networks and interconnections as generic. We have depicted the epidemic threshold curve for interconnected networks, considering the assumption that the infection could be initially present in either one or both of the networks. If the normalized infection strengths are above the threshold curve, the infection spreads, whereas if the normalized infection strengths are below the threshold curve, the disease does not spread. This is true for any level of interconnection. In the second part, we investigate the spillover phenomenon, where the disease in a novel host population network comes from a reservoir network. We have observed a clear phase transition when the number of links or the inter-network infection rate exceeds a certain threshold, keeping all other parameters constant. We observe two regimes for spillover: a major spillover region and a minor spillover region based on interpopulation links (fraction of links between two networks) and inter-network infection strength (infection rate between reservoir and host network). If the interpopulation links and inter-network infection strength are in the major spillover region, the spillover probability is high, while if the former parameters are in the minor spillover region, the spillover probability is low. When the number of infected individuals within a reservoir network is nearly equal, and the inter-network infection strength remains constant, the threshold number of links required to achieve the spillover threshold condition varies based on the network topology. Overall, this work contributes to the understanding of SIR dynamics in interconnected networks and sheds light on the behavior of epidemics in complex systems.

show abstract

Section: Introductionmentioning

confidence: 99%

SIR Epidemics in Interconnected Networks: threshold curve and phase transition

Das,

Scoglio

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Regarding robustness, interdependent modular networks are known to be much more vulnerable than typical graphs, and localized disruptions can give rise to powerful avalanches in power grids, biological, and financial networks [16,22,23]. Phase transitions in modular networks from a connected to a dismantled phase are more abrupt than in typical graphs.…”

Section: Introductionmentioning

confidence: 99%

Empirical determination of the optimal attack for fragmentation of modular networks

Abreu¹,

Gonçalves²,

Cunha³

2021

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

“…Regarding robustness, interdependent modular networks are known to be much more vulnerable than typical graphs, and localized disruptions can give rise to powerful avalanches in power grids, biological, and financial networks [16,17]. Phase transitions in modular networks from a connected to a dismantled phase are more abrupt than in typical graphs.…”

Section: Introductionmentioning

confidence: 99%

Empirical determination of the optimum attack for fragmentation of modular networks

Abreu¹,

Cunha²,

Gonçalves³

2018

Preprint

View full text Add to dashboard Cite

All possible removals of n = 5 nodes from networks of size N = 100 are performed in order to find the optimal set of nodes which fragments the original network into the smallest largest connected component. The resulting attacks are ordered according to the size of the largest connected component and compared with the state of the art methods of network attacks. We chose attacks of size 5 on relative small networks of size 100 because the number of all possible attacks, 100 5 ≈ 10 8 , is at the verge of the possible to compute with the available standard computers. Besides, we applied the procedure in a series of networks with controlled and varied modularity, comparing the resulting statistics with the effect of removing the same amount of vertices, according to the known most efficient disruption strategies, i.e., High Betweenness Adaptive attack (HBA), Collective Index attack (CI), and Modular Based Attack (MBA). Results show that modularity has an inverse relation with robustness, with Q c ≈ 0.7 being the critical value. For modularities lower than Q c , all heuristic method gives mostly the same results than with random attacks, while for bigger Q, networks are less robust and highly vulnerable to malicious attacks.

show abstract

Spreading of Failures in Interdependent Networks

Cited by 5 publications

References 66 publications

SIR Epidemics in Interconnected Networks: threshold curve and phase transition

SIR Epidemics in Interconnected Networks: threshold curve and phase transition

Empirical determination of the optimal attack for fragmentation of modular networks

Empirical determination of the optimum attack for fragmentation of modular networks

Contact Info

Product

Resources

About