• Corpus ID: 249209663

The $K$-core structure of complex networks with node reinforcement

@inproceedings{Ma2022TheS,
  title={The \$K\$-core structure of complex networks with node reinforcement},
  author={Rui Ma and Ya Yi Hu and Jin-Hua Zhao},
  year={2022}
}
Percolation theory provides a quantitative framework to estimate and enhance robustness of complex networked systems. A typical nonstructural method to improve network robustness is to introduce reinforced nodes, which function even in failure propagation. In the current percolation models for network robustness, giant connected component (GCC) is adopted as the main order parameter of macroscopic structural connectedness. Yet there still lacks a systematic evaluation how mesoscopic network… 
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References

SHOWING 1-10 OF 41 REFERENCES

Generalized k-core pruning process on directed networks

It is shown that the discontinuous transitions rise for cases with kin⩽2 or kou⩾2, and the unidirectional interactions among nodes drive the networks more vulnerable against perturbations based on in- and out-degrees separately, which can predict the relative sizes of residual node clusters on uncorrelated directed random graphs.

Eradicating catastrophic collapse in interdependent networks via reinforced nodes

A generalized percolation model is proposed and studied that introduces a fraction of reinforced nodes in the interdependent networks that can function and support their neighborhood and finds that the reinforced nodes reduce significantly the cascading failures in inter dependent networks system.

Percolation on complex networks: Theory and application

K-core Organization of Complex Networks

It is shown that in networks with a finite mean number zeta2 of the second-nearest neighbors, the emergence of a k-core is a hybrid phase transition, and in contrast, ifZeta2 diverges, the networks contain an infinite sequence of k-cores which are ultrarobust against random damage.

Inducing effect on the percolation transition in complex networks.

This work precisely predicts the percolation threshold and core size for uncorrelated random networks with arbitrary degree distributions for low-dimensional lattices and the core sizes of real-world networks can be well predicted using degree distribution as the only input.

Network robustness and fragility: percolation on random graphs.

This paper studies percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolators, bond percolations, and models in which occupation probabilities depend on vertex degree.

A model of Internet topology using k-shell decomposition

This analysis uses information on the connectivity of the network shells to separate, in a unique (no parameters) way, the Internet into three subcomponents: a nucleus that is a small, very well connected globally distributed subgraph; a fractal subcomponent that is able to connect the bulk of the Internet without congesting the nucleus, with self-similar properties and critical exponents predicted from percolation theory.

Eradicating abrupt collapse on single network with dependency groups.

For single-layer networks where nodes are grouped and nodes within the same group are dependent on each other, it is found that it is already enough to prevent abrupt catastrophic collapses by randomly reinforcing a constant density of nodes.

Interdependent networks: reducing the coupling strength leads to a change from a first to second order percolation transition.

It is shown both analytically and numerically that reducing the coupling between the networks leads to a change from a first order percolation phase transition to a second orderpercolation transition at a critical point.

Catastrophic cascade of failures in interdependent networks

This work develops a framework for understanding the robustness of interacting networks subject to cascading failures and presents exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks.