5,797 research outputs found
Congestion and centrality in traffic flow on complex networks
The central points of communication network flow has often been identified
using graph theoretical centrality measures. In real networks, the state of
traffic density arises from an interplay between the dynamics of the flow and
the underlying network structure. In this work we investigate the relationship
between centrality measures and the density of traffic for some simple particle
hopping models on networks with emerging scale-free degree distributions. We
also study how the speed of the dynamics are affected by the underlying network
structure. Among other conclusions, we find that, even at low traffic
densities, the dynamical measure of traffic density (the occupation ratio) has
a non-trivial dependence on the static centrality (quantified by "betweenness
centrality"), which non-central vertices getting a comparatively large portion
of the traffic.Comment: To appear in Advances in Complex System
Oscillations of complex networks
A complex network processing information or physical flows is usually
characterized by a number of macroscopic quantities such as the diameter and
the betweenness centrality. An issue of significant theoretical and practical
interest is how such a network responds to sudden changes caused by attacks or
disturbances. By introducing a model to address this issue, we find that, for a
finite-capacity network, perturbations can cause the network to
\emph{oscillate} persistently in the sense that the characterizing quantities
vary periodically or randomly with time. We provide a theoretical estimate of
the critical capacity-parameter value for the onset of the network oscillation.
The finding is expected to have broad implications as it suggests that complex
networks may be structurally highly dynamic.Comment: 4 pages, 4 figures. submitte
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
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