2,479 research outputs found
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Non-Markovian temporal networks with auto- and cross-correlated link dynamics
Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often observed to be non-Markovian, and the dynamics of their links are often correlated. Hence, to accurately model these networks, predict their evolution, and understand how information and other relevant quantities propagate over them, the inclusion of both memory and dynamical dependencies between links is key. In this article we introduce a general class of models of temporal networks based on discrete autoregressive processes for link dynamics. As a concrete and useful case study, we then concentrate on a specific model within this class, which allows to generate temporal networks with a specified underlying structural backbone, and with precise control over the dynamical dependencies between links and the strength and length of their memories. In this network model the presence of each link is influenced not only by its past activity, but also by the past activities of other links, as specified by a coupling matrix, which directly controls the causal relations, and hence the correlations, among links. We propose a maximum likelihood method for estimating the model's parameters from data, showing how the model allows a more realistic description of real-world temporal networks and also to predict their evolution. Due to the flexibility of maximum likelihood inference, we illustrate how to deal with heterogeneity and time-varying patterns, possibly including also nonstationary network dynamics. We then use our network model to investigate the role that, both the features of memory and the type of correlations in the dynamics of links have on the properties of processes occurring over a temporal network. Namely, we study the speed of a spreading process, as measured by the time it takes for diffusion to reach equilibrium. Through both numerical simulations and analytical results, we are able to separate the roles of autocorrelations and neighborhood correlations in link dynamics, showing that not only is the speed of diffusion nonmonotonically dependent on the memory length, but also that correlations among neighboring links help to speed up the spreading process, while autocorrelations slow it back down. Our results have implications in the study of opinion formation, the modeling of social networks, and the spreading of epidemics through mobile populations
Building surrogate temporal network data from observed backbones
In many data sets, crucial elements co-exist with non-essential ones and
noise. For data represented as networks in particular, several methods have
been proposed to extract a "network backbone", i.e., the set of most important
links. However, the question of how the resulting compressed views of the data
can effectively be used has not been tackled. Here we address this issue by
putting forward and exploring several systematic procedures to build surrogate
data from various kinds of temporal network backbones. In particular, we
explore how much information about the original data need to be retained
alongside the backbone so that the surrogate data can be used in data-driven
numerical simulations of spreading processes. We illustrate our results using
empirical temporal networks with a broad variety of structures and properties
The Structure of Information Pathways in a Social Communication Network
Social networks are of interest to researchers in part because they are
thought to mediate the flow of information in communities and organizations.
Here we study the temporal dynamics of communication using on-line data,
including e-mail communication among the faculty and staff of a large
university over a two-year period. We formulate a temporal notion of "distance"
in the underlying social network by measuring the minimum time required for
information to spread from one node to another -- a concept that draws on the
notion of vector-clocks from the study of distributed computing systems. We
find that such temporal measures provide structural insights that are not
apparent from analyses of the pure social network topology. In particular, we
define the network backbone to be the subgraph consisting of edges on which
information has the potential to flow the quickest. We find that the backbone
is a sparse graph with a concentration of both highly embedded edges and
long-range bridges -- a finding that sheds new light on the relationship
between tie strength and connectivity in social networks.Comment: 9 pages, 10 figures, to appear in Proceedings of the 14th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining (KDD'08),
August 24-27, 2008, Las Vegas, Nevada, US
Economic barriers to development : cost of access to internet infrastructure
The Internet is increasingly viewed as an "indispensable" resource for general development and economic growth (UNDP 1999). Its adoption by governments, organizations and individuals has resulted in the shrinking of spatial and temporal distances between different regions of the world, and has greatly facilitated the "free" and quick exchange of information. Such constrictions of time and space impact upon social and economic interactions at all levels of society. Furthermore, ramifications of this impact are felt by a society, group or individual irrespective of whether or not they use the Internet. The ability to access the Internet, and in particular the costs associated with such access, are therefore important points of consideration. Not only do these costs contribute to the disproportional spread of the Internet across the world's population; they also potentially contribute to uneven patterns of development within, and between countries
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