59 research outputs found
Complex Grid Computing
This article investigates the performance of grid computing systems whose
interconnections are given by random and scale-free complex network models.
Regular networks, which are common in parallel computing architectures, are
also used as a standard for comparison. The processing load is assigned to the
processing nodes on demand, and the efficiency of the overall computing is
quantified in terms of the respective speed-ups. It is found that random
networks allow higher computing efficiency than their scale-free counterparts
as a consequence of the smaller number of isolated clusters implied by the
former model. At the same time, for fixed cluster sizes, the scale free model
tend to provide slightly better efficiency. Two modifications of the random and
scale free paradigms, where new connections tend to favor more recently added
nodes, are proposed and shown to be more effective for grid computing than the
standard models. A well-defined correlation is observed between the topological
properties of the network and their respective computing efficiency.Comment: 5 pages, 2 figure
Evaluating links through spectral decomposition
Spectral decomposition has been rarely used to investigate complex networks.
In this work we apply this concept in order to define two types of
link-directed attacks while quantifying their respective effects on the
topology. Several other types of more traditional attacks are also adopted and
compared. These attacks had substantially diverse effects, depending on each
specific network (models and real-world structures). It is also showed that the
spectral-based attacks have special effect in affecting the transitivity of the
networks
On the Efficiency of Data Representation on the Modeling and Characterization of Complex Networks
Specific choices about how to represent complex networks can have a
substantial effect on the execution time required for the respective
construction and analysis of those structures. In this work we report a
comparison of the effects of representing complex networks statically as
matrices or dynamically as spase structures. Three theoretical models of
complex networks are considered: two types of Erdos-Renyi as well as the
Barabasi-Albert model. We investigated the effect of the different
representations with respect to the construction and measurement of several
topological properties (i.e. degree, clustering coefficient, shortest path
length, and betweenness centrality). We found that different forms of
representation generally have a substantial effect on the execution time, with
the sparse representation frequently resulting in remarkably superior
performance
Border trees of complex networks
The comprehensive characterization of the structure of complex networks is
essential to understand the dynamical processes which guide their evolution.
The discovery of the scale-free distribution and the small world property of
real networks were fundamental to stimulate more realistic models and to
understand some dynamical processes such as network growth. However, properties
related to the network borders (nodes with degree equal to one), one of its
most fragile parts, remain little investigated and understood. The border nodes
may be involved in the evolution of structures such as geographical networks.
Here we analyze complex networks by looking for border trees, which are defined
as the subgraphs without cycles connected to the remainder of the network
(containing cycles) and terminating into border nodes. In addition to
describing an algorithm for identification of such tree subgraphs, we also
consider a series of their measurements, including their number of vertices,
number of leaves, and depth. We investigate the properties of border trees for
several theoretical models as well as real-world networks.Comment: 5 pages, 1 figure, 2 tables. A working manuscript, comments and
suggestions welcome
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