72,865 research outputs found
From calls to communities: a model for time varying social networks
Social interactions vary in time and appear to be driven by intrinsic
mechanisms, which in turn shape the emerging structure of the social network.
Large-scale empirical observations of social interaction structure have become
possible only recently, and modelling their dynamics is an actual challenge.
Here we propose a temporal network model which builds on the framework of
activity-driven time-varying networks with memory. The model also integrates
key mechanisms that drive the formation of social ties - social reinforcement,
focal closure and cyclic closure, which have been shown to give rise to
community structure and the global connectedness of the network. We compare the
proposed model with a real-world time-varying network of mobile phone
communication and show that they share several characteristics from
heterogeneous degrees and weights to rich community structure. Further, the
strong and weak ties that emerge from the model follow similar weight-topology
correlations as real-world social networks, including the role of weak ties.Comment: 10 pages, 5 figure
Predicting epidemic risk from past temporal contact data
Understanding how epidemics spread in a system is a crucial step to prevent
and control outbreaks, with broad implications on the system's functioning,
health, and associated costs. This can be achieved by identifying the elements
at higher risk of infection and implementing targeted surveillance and control
measures. One important ingredient to consider is the pattern of
disease-transmission contacts among the elements, however lack of data or
delays in providing updated records may hinder its use, especially for
time-varying patterns. Here we explore to what extent it is possible to use
past temporal data of a system's pattern of contacts to predict the risk of
infection of its elements during an emerging outbreak, in absence of updated
data. We focus on two real-world temporal systems; a livestock displacements
trade network among animal holdings, and a network of sexual encounters in
high-end prostitution. We define the node's loyalty as a local measure of its
tendency to maintain contacts with the same elements over time, and uncover
important non-trivial correlations with the node's epidemic risk. We show that
a risk assessment analysis incorporating this knowledge and based on past
structural and temporal pattern properties provides accurate predictions for
both systems. Its generalizability is tested by introducing a theoretical model
for generating synthetic temporal networks. High accuracy of our predictions is
recovered across different settings, while the amount of possible predictions
is system-specific. The proposed method can provide crucial information for the
setup of targeted intervention strategies.Comment: 24 pages, 5 figures + SI (18 pages, 15 figures
Temporal stability of network partitions
We present a method to find the best temporal partition at any time-scale and
rank the relevance of partitions found at different time-scales. This method is
based on random walkers coevolving with the network and as such constitutes a
generalization of partition stability to the case of temporal networks. We show
that, when applied to a toy model and real datasets, temporal stability
uncovers structures that are persistent over meaningful time-scales as well as
important isolated events, making it an effective tool to study both abrupt
changes and gradual evolution of a network mesoscopic structures.Comment: 15 pages, 12 figure
Evolving spiking neural networks for temporal pattern recognition in the presence of noise
Creative Commons - Attribution-NonCommercial-NoDerivs 3.0 United StatesNervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noiseFinal Published versio
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