19 research outputs found
Real-Time Locating System to study the persistence of sociality in large-mammal group dynamics
Je n'ai pas encore les pages des proceedingsReal-Time Locating System to study the persistence of sociality in large-mammal group dynamics. European Conference on Precisions Livestock Farming (ECPLF
Dynamic communicability predicts infectiousness
Using real, time-dependent social interaction data, we look at correlations between some recently proposed dynamic centrality measures and summaries from large-scale epidemic simulations. The evolving network arises from email exchanges. The centrality measures, which are relatively inexpensive to compute, assign rankings to individual nodes based on their ability to broadcast information over the dynamic topology. We compare these with node rankings based on infectiousness that arise when a full stochastic SI simulation is performed over the dynamic network. More precisely, we look at the proportion of the network that a node is able to infect over a fixed time period, and the length of time that it takes for a node to infect half the network.We find that the dynamic centrality measures are an excellent, and inexpensive, proxy for the full simulation-based measures
Random Walks on Stochastic Temporal Networks
In the study of dynamical processes on networks, there has been intense focus
on network structure -- i.e., the arrangement of edges and their associated
weights -- but the effects of the temporal patterns of edges remains poorly
understood. In this chapter, we develop a mathematical framework for random
walks on temporal networks using an approach that provides a compromise between
abstract but unrealistic models and data-driven but non-mathematical
approaches. To do this, we introduce a stochastic model for temporal networks
in which we summarize the temporal and structural organization of a system
using a matrix of waiting-time distributions. We show that random walks on
stochastic temporal networks can be described exactly by an
integro-differential master equation and derive an analytical expression for
its asymptotic steady state. We also discuss how our work might be useful to
help build centrality measures for temporal networks.Comment: Chapter in Temporal Networks (Petter Holme and Jari Saramaki
editors). Springer. Berlin, Heidelberg 2013. The book chapter contains minor
corrections and modifications. This chapter is based on arXiv:1112.3324,
which contains additional calculations and numerical simulation
Modern temporal network theory: A colloquium
The power of any kind of network approach lies in the ability to simplify a
complex system so that one can better understand its function as a whole.
Sometimes it is beneficial, however, to include more information than in a
simple graph of only nodes and links. Adding information about times of
interactions can make predictions and mechanistic understanding more accurate.
The drawback, however, is that there are not so many methods available, partly
because temporal networks is a relatively young field, partly because it more
difficult to develop such methods compared to for static networks. In this
colloquium, we review the methods to analyze and model temporal networks and
processes taking place on them, focusing mainly on the last three years. This
includes the spreading of infectious disease, opinions, rumors, in social
networks; information packets in computer networks; various types of signaling
in biology, and more. We also discuss future directions.Comment: Final accepted versio
Hepatitis B screening among immigrants: How to successfully reach the Moroccan community
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Burstiness and fractional diffusion on complex networks
Many dynamical processes on real world networks display complex temporal
patterns as, for instance, a fat-tailed distribution of inter-events times,
leading to heterogeneous waiting times between events. In this work, we focus
on distributions whose average inter-event time diverges, and study its impact
on the dynamics of random walkers on networks. The process can naturally be
described, in the long time limit, in terms of Riemann-Liouville fractional
derivatives. We show that all the dynamical modes possess, in the asymptotic
regime, the same power law relaxation, which implies that the dynamics does not
exhibit time-scale separation between modes, and that no mode can be neglected
versus another one, even for long times. Our results are then confirmed by
numerical simulations.Comment: 7 pages, 4 figure