4,953 research outputs found
k-core (bootstrap) percolation on complex networks: Critical phenomena and nonlocal effects
We develop the theory of the k-core (bootstrap) percolation on uncorrelated
random networks with arbitrary degree distributions. We show that the k-core
percolation is an unusual, hybrid phase transition with a jump emergence of the
k-core as at a first order phase transition but also with a critical
singularity as at a continuous transition. We describe the properties of the
k-core, explain the meaning of the order parameter for the k-core percolation,
and reveal the origin of the specific critical phenomena. We demonstrate that a
so-called ``corona'' of the k-core plays a crucial role (corona is a subset of
vertices in the k-core which have exactly k neighbors in the k-core). It turns
out that the k-core percolation threshold is at the same time the percolation
threshold of finite corona clusters. The mean separation of vertices in corona
clusters plays the role of the correlation length and diverges at the critical
point. We show that a random removal of even one vertex from the k-core may
result in the collapse of a vast region of the k-core around the removed
vertex. The mean size of this region diverges at the critical point. We find an
exact mapping of the k-core percolation to a model of cooperative relaxation.
This model undergoes critical relaxation with a divergent rate at some critical
moment.Comment: 11 pages, 8 figure
The k-core and branching processes
The k-core of a graph G is the maximal subgraph of G having minimum degree at
least k. In 1996, Pittel, Spencer and Wormald found the threshold
for the emergence of a non-trivial k-core in the random graph ,
and the asymptotic size of the k-core above the threshold. We give a new proof
of this result using a local coupling of the graph to a suitable branching
process. This proof extends to a general model of inhomogeneous random graphs
with independence between the edges. As an example, we study the k-core in a
certain power-law or `scale-free' graph with a parameter c controlling the
overall density of edges. For each k at least 3, we find the threshold value of
c at which the k-core emerges, and the fraction of vertices in the k-core when
c is \epsilon above the threshold. In contrast to , this
fraction tends to 0 as \epsilon tends to 0.Comment: 30 pages, 1 figure. Minor revisions. To appear in Combinatorics,
Probability and Computin
k-core organization of complex networks
We analytically describe the architecture of randomly damaged uncorrelated
networks as a set of successively enclosed substructures -- k-cores. The k-core
is the largest subgraph where vertices have at least k interconnections. We
find the structure of k-cores, their sizes, and their birth points -- the
bootstrap percolation thresholds. We show that in networks with a finite mean
number z_2 of the second-nearest neighbors, the emergence of a k-core is a
hybrid phase transition. In contrast, if z_2 diverges, the networks contain an
infinite sequence of k-cores which are ultra-robust against random damage.Comment: 5 pages, 3 figure
Heterogeneous-k-core versus Bootstrap Percolation on Complex Networks
We introduce the heterogeneous--core, which generalizes the -core, and
contrast it with bootstrap percolation. Vertices have a threshold which
may be different at each vertex. If a vertex has less than neighbors it
is pruned from the network. The heterogeneous--core is the sub-graph
remaining after no further vertices can be pruned. If the thresholds are
with probability or with probability , the process
forms one branch of an activation-pruning process which demonstrates
hysteresis. The other branch is formed by ordinary bootstrap percolation. We
show that there are two types of transitions in this heterogeneous--core
process: the giant heterogeneous--core may appear with a continuous
transition and there may be a second, discontinuous, hybrid transition. We
compare critical phenomena, critical clusters and avalanches at the
heterogeneous--core and bootstrap percolation transitions. We also show that
network structure has a crucial effect on these processes, with the giant
heterogeneous--core appearing immediately at a finite value for any
when the degree distribution tends to a power law with
.Comment: 10 pages, 4 figure
K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases
We consider the -core decomposition of network models and Internet graphs
at the autonomous system (AS) level. The -core analysis allows to
characterize networks beyond the degree distribution and uncover structural
properties and hierarchies due to the specific architecture of the system. We
compare the -core structure obtained for AS graphs with those of several
network models and discuss the differences and similarities with the real
Internet architecture. The presence of biases and the incompleteness of the
real maps are discussed and their effect on the -core analysis is assessed
with numerical experiments simulating biased exploration on a wide range of
network models. We find that the -core analysis provides an interesting
characterization of the fluctuations and incompleteness of maps as well as
information helping to discriminate the original underlying structure
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