2,827 research outputs found
Scale-free network clustering in hyperbolic and other random graphs
Random graphs with power-law degrees can model scale-free networks as sparse
topologies with strong degree heterogeneity. Mathematical analysis of such
random graphs proved successful in explaining scale-free network properties
such as resilience, navigability and small distances. We introduce a
variational principle to explain how vertices tend to cluster in triangles as a
function of their degrees. We apply the variational principle to the hyperbolic
model that quickly gains popularity as a model for scale-free networks with
latent geometries and clustering. We show that clustering in the hyperbolic
model is non-vanishing and self-averaging, so that a single random graph sample
is a good representation in the large-network limit. We also demonstrate the
variational principle for some classical random graphs including the
preferential attachment model and the configuration model
Hyperbolic Geometry of Complex Networks
We develop a geometric framework to study the structure and function of
complex networks. We assume that hyperbolic geometry underlies these networks,
and we show that with this assumption, heterogeneous degree distributions and
strong clustering in complex networks emerge naturally as simple reflections of
the negative curvature and metric property of the underlying hyperbolic
geometry. Conversely, we show that if a network has some metric structure, and
if the network degree distribution is heterogeneous, then the network has an
effective hyperbolic geometry underneath. We then establish a mapping between
our geometric framework and statistical mechanics of complex networks. This
mapping interprets edges in a network as non-interacting fermions whose
energies are hyperbolic distances between nodes, while the auxiliary fields
coupled to edges are linear functions of these energies or distances. The
geometric network ensemble subsumes the standard configuration model and
classical random graphs as two limiting cases with degenerate geometric
structures. Finally, we show that targeted transport processes without global
topology knowledge, made possible by our geometric framework, are maximally
efficient, according to all efficiency measures, in networks with strongest
heterogeneity and clustering, and that this efficiency is remarkably robust
with respect to even catastrophic disturbances and damages to the network
structure
Network Cosmology
Prediction and control of the dynamics of complex networks is a central
problem in network science. Structural and dynamical similarities of different
real networks suggest that some universal laws might accurately describe the
dynamics of these networks, albeit the nature and common origin of such laws
remain elusive. Here we show that the causal network representing the
large-scale structure of spacetime in our accelerating universe is a power-law
graph with strong clustering, similar to many complex networks such as the
Internet, social, or biological networks. We prove that this structural
similarity is a consequence of the asymptotic equivalence between the
large-scale growth dynamics of complex networks and causal networks. This
equivalence suggests that unexpectedly similar laws govern the dynamics of
complex networks and spacetime in the universe, with implications to network
science and cosmology
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