5,005 research outputs found
Degree-degree correlations in random graphs with heavy-tailed degrees
Mixing patterns in large self-organizing networks, such as the Internet, the
World Wide Web, social and biological networks are often characterized by
degree-degree {dependencies} between neighbouring nodes. One of the problems
with the commonly used Pearson's correlation coefficient (termed as the
assortativity coefficient) is that {in disassortative networks its magnitude
decreases} with the network size. This makes it impossible to compare mixing
patterns, for example, in two web crawls of different size.
We start with a simple model of two heavy-tailed highly correlated random
variable and , and show that the sample correlation coefficient
converges in distribution either to a proper random variable on , or to
zero, and if then the limit is non-negative. We next show that it is
non-negative in the large graph limit when the degree distribution has an
infinite third moment. We consider the alternative degree-degree dependency
measure, based on the Spearman's rho, and prove that it converges to an
appropriate limit under very general conditions. We verify that these
conditions hold in common network models, such as configuration model and
Preferential Attachment model. We conclude that rank correlations provide a
suitable and informative method for uncovering network mixing patterns
Weighted distances in scale-free configuration models
In this paper we study first-passage percolation in the configuration model
with empirical degree distribution that follows a power-law with exponent . We assign independent and identically distributed (i.i.d.)\ weights
to the edges of the graph. We investigate the weighted distance (the length of
the shortest weighted path) between two uniformly chosen vertices, called
typical distances. When the underlying age-dependent branching process
approximating the local neighborhoods of vertices is found to produce
infinitely many individuals in finite time -- called explosive branching
process -- Baroni, Hofstad and the second author showed that typical distances
converge in distribution to a bounded random variable. The order of magnitude
of typical distances remained open for the case when the
underlying branching process is not explosive. We close this gap by determining
the first order of magnitude of typical distances in this regime for arbitrary,
not necessary continuous edge-weight distributions that produce a non-explosive
age-dependent branching process with infinite mean power-law offspring
distributions. This sequence tends to infinity with the amount of vertices,
and, by choosing an appropriate weight distribution, can be tuned to be any
growing function that is , where is the number of vertices
in the graph. We show that the result remains valid for the the erased
configuration model as well, where we delete loops and any second and further
edges between two vertices.Comment: 24 page
Diameters in preferential attachment models
In this paper, we investigate the diameter in preferential attachment (PA-)
models, thus quantifying the statement that these models are small worlds. The
models studied here are such that edges are attached to older vertices
proportional to the degree plus a constant, i.e., we consider affine PA-models.
There is a substantial amount of literature proving that, quite generally,
PA-graphs possess power-law degree sequences with a power-law exponent \tau>2.
We prove that the diameter of the PA-model is bounded above by a constant
times \log{t}, where t is the size of the graph. When the power-law exponent
\tau exceeds 3, then we prove that \log{t} is the right order, by proving a
lower bound of this order, both for the diameter as well as for the typical
distance. This shows that, for \tau>3, distances are of the order \log{t}. For
\tau\in (2,3), we improve the upper bound to a constant times \log\log{t}, and
prove a lower bound of the same order for the diameter. Unfortunately, this
proof does not extend to typical distances. These results do show that the
diameter is of order \log\log{t}.
These bounds partially prove predictions by physicists that the typical
distance in PA-graphs are similar to the ones in other scale-free random
graphs, such as the configuration model and various inhomogeneous random graph
models, where typical distances have been shown to be of order \log\log{t} when
\tau\in (2,3), and of order \log{t} when \tau>3
Degrees and distances in random and evolving Apollonian networks
This paper studies Random and Evolving Apollonian networks (RANs and EANs),
in d dimension for any d>=2, i.e. dynamically evolving random d dimensional
simplices looked as graphs inside an initial d-dimensional simplex. We
determine the limiting degree distribution in RANs and show that it follows a
power law tail with exponent tau=(2d-1)/(d-1). We further show that the degree
distribution in EANs converges to the same degree distribution if the
simplex-occupation parameter in the n-th step of the dynamics is q_n->0 and
sum_{n=0}^infty q_n =infty. This result gives a rigorous proof for the
conjecture of Zhang et al. that EANs tend to show similar behavior as RANs once
the occupation parameter q->0. We also determine the asymptotic behavior of
shortest paths in RANs and EANs for arbitrary d dimensions. For RANs we show
that the shortest path between two uniformly chosen vertices (typical
distance), the flooding time of a uniformly picked vertex and the diameter of
the graph after n steps all scale as constant times log n. We determine the
constants for all three cases and prove a central limit theorem for the typical
distances. We prove a similar CLT for typical distances in EANs
Provable and practical approximations for the degree distribution using sublinear graph samples
The degree distribution is one of the most fundamental properties used in the
analysis of massive graphs. There is a large literature on graph sampling,
where the goal is to estimate properties (especially the degree distribution)
of a large graph through a small, random sample. The degree distribution
estimation poses a significant challenge, due to its heavy-tailed nature and
the large variance in degrees.
We design a new algorithm, SADDLES, for this problem, using recent
mathematical techniques from the field of sublinear algorithms. The SADDLES
algorithm gives provably accurate outputs for all values of the degree
distribution. For the analysis, we define two fatness measures of the degree
distribution, called the -index and the -index. We prove that SADDLES is
sublinear in the graph size when these indices are large. A corollary of this
result is a provably sublinear algorithm for any degree distribution bounded
below by a power law.
We deploy our new algorithm on a variety of real datasets and demonstrate its
excellent empirical behavior. In all instances, we get extremely accurate
approximations for all values in the degree distribution by observing at most
of the vertices. This is a major improvement over the state-of-the-art
sampling algorithms, which typically sample more than of the vertices to
give comparable results. We also observe that the and -indices of real
graphs are large, validating our theoretical analysis.Comment: Longer version of the WWW 2018 submissio
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