Interdependent networks with correlated degrees of mutually dependent nodes
Sergey V. Buldyrev, Nathaniel Shere, and Gabriel A. Cwilich

TL;DR
This paper analyzes the robustness and percolation properties of interdependent networks with correlated degrees, revealing how degree distribution affects the nature of phase transitions and network resilience.
Contribution
It introduces the concept of correspondently coupled networks (CCN) with correlated degrees and derives analytical results for their percolation thresholds and transition types.
Findings
Percolation transition occurs at a lower fraction in CCN compared to randomly coupled networks.
The transition is first order if the degree distribution has a finite second moment.
For scale-free networks with 2<λ≤3, the transition becomes second order or occurs at zero threshold.
Abstract
We study a problem of failure of two interdependent networks in the case of correlated degrees of mutually dependent nodes. We assume that both networks (A and B) have the same number of nodes connected by the bidirectional dependency links establishing a one-to-one correspondence between the nodes of the two networks in a such a way that the mutually dependent nodes have the same number of connectivity links, i.e. their degrees coincide. This implies that both networks have the same degree distribution . We call such networks correspondently coupled networks (CCN). We assume that the nodes in each network are randomly connected. We define the mutually connected clusters and the mutual giant component as in earlier works on randomly coupled interdependent networks and assume that only the nodes which belong to the mutual giant component remain functional. We assume that…
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