29,180 research outputs found
Space and Time Efficient Parallel Graph Decomposition, Clustering, and Diameter Approximation
We develop a novel parallel decomposition strategy for unweighted, undirected
graphs, based on growing disjoint connected clusters from batches of centers
progressively selected from yet uncovered nodes. With respect to similar
previous decompositions, our strategy exercises a tighter control on both the
number of clusters and their maximum radius.
We present two important applications of our parallel graph decomposition:
(1) -center clustering approximation; and (2) diameter approximation. In
both cases, we obtain algorithms which feature a polylogarithmic approximation
factor and are amenable to a distributed implementation that is geared for
massive (long-diameter) graphs. The total space needed for the computation is
linear in the problem size, and the parallel depth is substantially sublinear
in the diameter for graphs with low doubling dimension. To the best of our
knowledge, ours are the first parallel approximations for these problems which
achieve sub-diameter parallel time, for a relevant class of graphs, using only
linear space. Besides the theoretical guarantees, our algorithms allow for a
very simple implementation on clustered architectures: we report on extensive
experiments which demonstrate their effectiveness and efficiency on large
graphs as compared to alternative known approaches.Comment: 14 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
Generating realistic scaled complex networks
Research on generative models is a central project in the emerging field of
network science, and it studies how statistical patterns found in real networks
could be generated by formal rules. Output from these generative models is then
the basis for designing and evaluating computational methods on networks, and
for verification and simulation studies. During the last two decades, a variety
of models has been proposed with an ultimate goal of achieving comprehensive
realism for the generated networks. In this study, we (a) introduce a new
generator, termed ReCoN; (b) explore how ReCoN and some existing models can be
fitted to an original network to produce a structurally similar replica, (c)
use ReCoN to produce networks much larger than the original exemplar, and
finally (d) discuss open problems and promising research directions. In a
comparative experimental study, we find that ReCoN is often superior to many
other state-of-the-art network generation methods. We argue that ReCoN is a
scalable and effective tool for modeling a given network while preserving
important properties at both micro- and macroscopic scales, and for scaling the
exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the
paper was presented at the 5th International Workshop on Complex Networks and
their Application
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
Multiplicative Attribute Graph Model of Real-World Networks
Large scale real-world network data such as social and information networks
are ubiquitous. The study of such social and information networks seeks to find
patterns and explain their emergence through tractable models. In most
networks, and especially in social networks, nodes have a rich set of
attributes (e.g., age, gender) associated with them.
Here we present a model that we refer to as the Multiplicative Attribute
Graphs (MAG), which naturally captures the interactions between the network
structure and the node attributes. We consider a model where each node has a
vector of categorical latent attributes associated with it. The probability of
an edge between a pair of nodes then depends on the product of individual
attribute-attribute affinities. The model yields itself to mathematical
analysis and we derive thresholds for the connectivity and the emergence of the
giant connected component, and show that the model gives rise to networks with
a constant diameter. We analyze the degree distribution to show that MAG model
can produce networks with either log-normal or power-law degree distributions
depending on certain conditions.Comment: 33 pages, 6 figure
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