5,445 research outputs found
Connectivity of inhomogeneous random graphs
We find conditions for the connectivity of inhomogeneous random graphs with
intermediate density. Our results generalize the classical result for G(n, p),
when p = c log n/n. We draw n independent points X_i from a general
distribution on a separable metric space, and let their indices form the vertex
set of a graph. An edge (i,j) is added with probability min(1, \K(X_i,X_j) log
n/n), where \K \ge 0 is a fixed kernel. We show that, under reasonably weak
assumptions, the connectivity threshold of the model can be determined.Comment: 13 pages. To appear in Random Structures and Algorithm
On connectivity and robustness of random graphs with inhomogeneity
The study of threshold functions has a long history in random graph theory. It is known that the thresholds for minimum degree k, k-connectivity, as well as k-robustness coincide for a binomial random graph. In this paper we consider an inhomogeneous random graph model, which is obtained by including each possible edge independently with an individual probability. Based on an intuitive concept of neighborhood density, we show two sufficient conditions guaranteeing k-connectivity and k-robustness, respectively, which are asymptotically equivalent. Our framework sheds some light on extending uniform threshold values in homogeneous random graphs to threshold landscapes in inhomogeneous random graphs
Next nearest neighbour Ising models on random graphs
This paper develops results for the next nearest neighbour Ising model on
random graphs. Besides being an essential ingredient in classic models for
frustrated systems, second neighbour interactions interactions arise naturally
in several applications such as the colour diversity problem and graphical
games. We demonstrate ensembles of random graphs, including regular
connectivity graphs, that have a periodic variation of free energy, with either
the ratio of nearest to next nearest couplings, or the mean number of nearest
neighbours. When the coupling ratio is integer paramagnetic phases can be found
at zero temperature. This is shown to be related to the locked or unlocked
nature of the interactions. For anti-ferromagnetic couplings, spin glass phases
are demonstrated at low temperature. The interaction structure is formulated as
a factor graph, the solution on a tree is developed. The replica symmetric and
energetic one-step replica symmetry breaking solution is developed using the
cavity method. We calculate within these frameworks the phase diagram and
demonstrate the existence of dynamical transitions at zero temperature for
cases of anti-ferromagnetic coupling on regular and inhomogeneous random
graphs.Comment: 55 pages, 15 figures, version 2 with minor revisions, to be published
J. Stat. Mec
On the Giant Component of Geometric Inhomogeneous Random Graphs
In this paper we study the threshold model of geometric inhomogeneous random graphs (GIRGs); a generative random graph model that is closely related to hyperbolic random graphs (HRGs). These models have been observed to capture complex real-world networks well with respect to the structural and algorithmic properties. Following comprehensive studies regarding their connectivity, i.e., which parts of the graphs are connected, we have a good understanding under which circumstances a giant component (containing a constant fraction of the graph) emerges.
While previous results are rather technical and challenging to work with, the goal of this paper is to provide more accessible proofs. At the same time we significantly improve the previously known probabilistic guarantees, showing that GIRGs contain a giant component with probability 1 - exp(-?(n^{(3-?)/2})) for graph size n and a degree distribution with power-law exponent ? ? (2, 3). Based on that we additionally derive insights about the connectivity of certain induced subgraphs of GIRGs
On the Strength of Connectivity of Inhomogeneous Random K-out Graphs
Random graphs are an important tool for modelling and analyzing the
underlying properties of complex real-world networks. In this paper, we study a
class of random graphs known as the inhomogeneous random K-out graphs which
were recently introduced to analyze heterogeneous sensor networks secured by
the pairwise scheme. In this model, first, each of the nodes is classified
as type-1 (respectively, type-2) with probability (respectively,
independently from each other. Next, each type-1 (respectively,
type-2) node draws 1 arc towards a node (respectively, arcs towards
distinct nodes) selected uniformly at random, and then the orientation of the
arcs is ignored. From the literature on homogeneous K-out graphs wherein all
nodes select neighbors (i.e., ), it is known that when , the graph is -connected asymptotically almost surely (a.a.s.) as
gets large. In the inhomogeneous case (i.e., ), it was recently
established that achieving even 1-connectivity a.a.s. requires .
Here, we provide a comprehensive set of results to complement these existing
results. First, we establish a sharp zero-one law for -connectivity, showing
that for the network to be -connected a.a.s., we need to set for all .
Despite such large scaling of being required for -connectivity, we
show that the trivial condition of for all is sufficient to
ensure that inhomogeneous K-out graph has a connected component of size
whp
Inhomogeneous percolation models for spreading phenomena in random graphs
Percolation theory has been largely used in the study of structural
properties of complex networks such as the robustness, with remarkable results.
Nevertheless, a purely topological description is not sufficient for a correct
characterization of networks behaviour in relation with physical flows and
spreading phenomena taking place on them. The functionality of real networks
also depends on the ability of the nodes and the edges in bearing and handling
loads of flows, energy, information and other physical quantities. We propose
to study these properties introducing a process of inhomogeneous percolation,
in which both the nodes and the edges spread out the flows with a given
probability.
Generating functions approach is exploited in order to get a generalization
of the Molloy-Reed Criterion for inhomogeneous joint site bond percolation in
correlated random graphs. A series of simple assumptions allows the analysis of
more realistic situations, for which a number of new results are presented. In
particular, for the site percolation with inhomogeneous edge transmission, we
obtain the explicit expressions of the percolation threshold for many
interesting cases, that are analyzed by means of simple examples and numerical
simulations. Some possible applications are debated.Comment: 28 pages, 11 figure
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