Vertex Intrinsic Fitness: How to Produce Arbitrary Scale-Free Networks
Vito D. P. Servedio, Guido Caldarelli, Paolo Butta`
Abstract
We study a recent model of random networks based on the presence of an intrinsic character of the vertices called fitness. The vertices fitnesses are drawn from a given probability distribution density. The edges between pair of vertices are drawn according to a linking probability function depending on the fitnesses of the two vertices involved. We study here different choices for the probability distribution densities and the linking functions. We find that, irrespective of the particular choices, the generation of scale-free networks is straightforward. We then derive the general conditions under which scale-free behavior appears. This model could then represent a possible explanation for the ubiquity and robustness of such structures.
Create a lesson
Related papers
Robust Golden Ratio universality as a consequence of a circulant symmetry
Luka Paljk, Johannes Schmidt, Vladislav Popkov
Replica Transition at Measurement-Altered Ising Criticality
Kabir Khanna, Sara Murciano, Romain Vasseur
Scalene Yang-Baxter triples realize all nearest-neighbour Hamiltonians with global additive symmetries
Pramod Padmanabhan, Somnath Maity, Akash Sinha et al.
Graph-theoretic framework for semi-Markov networks: transient and stationary occupation probabilities, hitting times and weak ergodicity breaking
Julian Voits
Spectral optimization of spherical lattices and applications to classical and quantum interacting spin systems
Jonas Völler, Grigorios Makris, Fabian Hassler et al.
Entropic-ordered superfluid
Isabella Zane, Andrew Lucas