Critical Points of Correlated Percolation in a Gravitational Link-adding Network Model
Chen-Ping Zhu, Long-Tao Jia, Beom Jun Kim, Bing-Hong Wang and, H. E. Stanley

TL;DR
This paper introduces a correlated percolation model on 2D networks with a gravity-inspired connection probability, develops a method to identify critical points, and explores how parameters influence network percolation and scaling relations.
Contribution
It presents a novel gravity-based correlated percolation model, along with a new approach to determine critical points and insights into the effects of parameters on network evolution.
Findings
Critical points depend on link types and average lengths.
Decay exponent and transmission radius influence evolution pace.
New scaling relations are validated, violating Weinrib's law.
Abstract
Motivated by the importance of geometric information in real systems, a new model for long-range correlated percolation in link-adding networks is proposed with the connecting probability decaying with a power-law of the distance on the two-dimensional(2D) plane. By overlapping it with Achlioptas process, it serves as a gravity model which can be tuned to facilitate or inhibit the network percolation in a generic view, cover a broad range of thresholds. Moreover, it yields a set of new scaling relations. In the present work, we develop an approach to determine critical points for them by simulating the temporal evolutions of type-I, type-II and type-III links(chosen from both inter-cluster links, an intra-cluster link compared with an inter-cluster one, and both intra-cluster ones, respectively) and corresponding average lengths. Numerical results have revealed objective competition…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Theoretical and Computational Physics
