4,613 research outputs found
Small Worlds: Strong Clustering in Wireless Networks
Small-worlds represent efficient communication networks that obey two
distinguishing characteristics: a high clustering coefficient together with a
small characteristic path length. This paper focuses on an interesting paradox,
that removing links in a network can increase the overall clustering
coefficient. Reckful Roaming, as introduced in this paper, is a 2-localized
algorithm that takes advantage of this paradox in order to selectively remove
superfluous links, this way optimizing the clustering coefficient while still
retaining a sufficiently small characteristic path length.Comment: To appear in: 1st International Workshop on Localized Algorithms and
Protocols for Wireless Sensor Networks (LOCALGOS 2007), 2007, IEEE Compuster
Society Pres
Planar growth generates scale free networks
In this paper we introduce a model of spatial network growth in which nodes
are placed at randomly selected locations on a unit square in ,
forming new connections to old nodes subject to the constraint that edges do
not cross. The resulting network has a power law degree distribution, high
clustering and the small world property. We argue that these characteristics
are a consequence of the two defining features of the network formation
procedure; growth and planarity conservation. We demonstrate that the model can
be understood as a variant of random Apollonian growth and further propose a
one parameter family of models with the Random Apollonian Network and the
Deterministic Apollonian Network as extreme cases and our model as a midpoint
between them. We then relax the planarity constraint by allowing edge crossings
with some probability and find a smooth crossover from power law to exponential
degree distributions when this probability is increased.Comment: 27 pages, 9 figure
A Survey on the Network Models applied in the Industrial Network Optimization
Network architecture design is very important for the optimization of
industrial networks. The type of network architecture can be divided into
small-scale network and large-scale network according to its scale. Graph
theory is an efficient mathematical tool for network topology modeling. For
small-scale networks, its structure often has regular topology. For large-scale
networks, the existing research mainly focuses on the random characteristics of
network nodes and edges. Recently, popular models include random networks,
small-world networks and scale-free networks. Starting from the scale of
network, this survey summarizes and analyzes the network modeling methods based
on graph theory and the practical application in industrial scenarios.
Furthermore, this survey proposes a novel network performance metric - system
entropy. From the perspective of mathematical properties, the analysis of its
non-negativity, monotonicity and concave-convexity is given. The advantage of
system entropy is that it can cover the existing regular network, random
network, small-world network and scale-free network, and has strong generality.
The simulation results show that this metric can realize the comparison of
various industrial networks under different models.Comment: 26 pages, 11 figures, Journa
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