Emergent networks in fractional percolation
arXiv:2110.02385 · doi:10.1016/j.physa.2022.127057
Abstract
Real networks are vulnerable to random failures and malicious attacks. However, when a node is harmed or damaged, it may remain partially functional, which helps to maintain the overall network structure and functionality. In this paper, we study the network structure for a fractional percolation process [Shang, Phys. Rev. E 89, 012813 (2014)], in which the state of a node can be either fully functional (FF), partially functional (PF), or dysfunctional (D). We develop new equations to calculate the relative size of the percolating cluster of FF and PF nodes, that are in agreement with our stochastic simulations. In addition, we find a regime in which the percolating cluster can be described as a coarse-grained bipartite network, namely, as a set of finite groups of FF nodes connected by PF nodes. Moreover, these groups behave as a set of "supernodes" with a power-law degree distribution. Finally, we show how this emergent structure explains the values of several critical exponents around the percolation threshold.
References in corpus (16)
- Catastrophic cascade of failures in interdependent networks
- Resilience of the Internet to random breakdowns
- Breakdown of the Internet under intentional attack
- k-core organization of complex networks
- Recent advances in percolation theory and its applications
- Percolation Critical Exponents in Scale-Free Networks
- Bootstrap Percolation on Complex Networks
- Critical phenomena in networks
- Resilience of networks with community structure behaves as if under an external field
- Heterogeneous-k-core versus Bootstrap Percolation on Complex Networks
- Breaking of the site-bond percolation universality in networks
- Revealing the Micro-Structure of the Giant Component in Random Graph Ensembles
- Percolation on bipartite scale-free networks
- Color-avoiding percolation
- Statistical analysis of articulation points in configuration model networks
- Statistical analysis of edges and bredges in configuration model networks