6 research outputs found
Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
Given their importance in shaping social networks and determining how
information or diseases propagate in a population, human interactions are the
subject of many data collection efforts. To this aim, different methods are
commonly used, from diaries and surveys to wearable sensors. These methods show
advantages and limitations but are rarely compared in a given setting. As
surveys targeting friendship relations might suffer less from memory biases
than contact diaries, it is also interesting to explore how daily contact
patterns compare with friendship relations and with online social links. Here
we make progresses in these directions by leveraging data from a French high
school: face-to-face contacts measured by two concurrent methods, sensors and
diaries; self-reported friendship surveys; Facebook links. We compare the data
sets and find that most short contacts are not reported in diaries while long
contacts have larger reporting probability, with a general tendency to
overestimate durations. Measured contacts corresponding to reported friendship
can have durations of any length but all long contacts correspond to reported
friendships. Online links not associated to reported friendships correspond to
short face-to-face contacts, highlighting the different nature of reported
friendships and online links. Diaries and surveys suffer from a low sampling
rate, showing the higher acceptability of sensor-based platform. Despite the
biases, we found that the overall structure of the contact network, i.e., the
mixing patterns between classes, is correctly captured by both self-reported
contacts and friendships networks. Overall, diaries and surveys tend to yield a
correct picture of the structural organization of the contact network, albeit
with much less links, and give access to a sort of backbone of the contact
network corresponding to the strongest links in terms of cumulative durations
Compensating for population sampling in simulations of epidemic spread on temporal contact networks
Data describing human interactions often suffer from incomplete sampling of
the underlying population. As a consequence, the study of contagion processes
using data-driven models can lead to a severe underestimation of the epidemic
risk. Here we present a systematic method to alleviate this issue and obtain a
better estimation of the risk in the context of epidemic models informed by
high-resolution time-resolved contact data. We consider several such data sets
collected in various contexts and perform controlled resampling experiments. We
show how the statistical information contained in the resampled data can be
used to build a series of surrogate versions of the unknown contacts. We
simulate epidemic processes on the resulting reconstructed data sets and show
that it is possible to obtain good estimates of the outcome of simulations
performed using the complete data set. We discuss limitations and potential
improvements of our method
The structured backbone of temporal social ties
In many data sets, crucial information on the structure and temporality of a
system coexists with noise and non-essential elements. In networked systems,
for instance, some edges might be non-essential or exist only by chance.
Filtering them out and extracting a set of relevant connections, the "network
backbone", is a non-trivial task, and methods put forward until now do not
address time-resolved networks, whose availability has strongly increased in
recent years. We develop here such a method, by defining an adequate temporal
network null model, which calculates the random chance of nodes to be connected
at any time after controlling for their activity. This allows us to identify,
at any level of statistical significance, pairs of nodes that have more
interactions than expected given their activities: These form a backbone of
significant ties. We apply our method to empirical temporal networks of
socio-economic interest and find that (i) at given level of statistical
significance, our method identifies more significant ties than methods
considering temporally aggregated networks, and (ii) when a community structure
is present, most significant ties are intra-community edges, suggesting that
the weights of inter-community edges can be explained by the null model of
random interactions. Most importantly, our filtering method can assign a
significance to more complex structures such as triads of simultaneous
interactions, while methods based on static representations are by construction
unable to do so. Strikingly, we uncover that significant triads are not
equivalent to triangles composed by three significant edges. Our results hint
at new ways to represent temporal networks for use in data-driven models and in
anonymity-preserving ways.Comment: Main text: 18 pages, 6 figures. SI: 22 pages, 17 figure
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
Recommended from our members
Networked Dynamical Systems: Privacy, Control, and Cognition
Many natural and man-made systems, ranging from thenervous system to power and transportation grids to societies, exhibitdynamic behaviors that evolve over a sparse and complex network. This networked aspect raises significant challenges and opportunities for the identification, analysis, and control of such dynamic behaviors. While some of these challenges emanate from the networked aspect \emph{per se} (such as the sparsity of connections between system components and the interplay between nodal \emph{communication} and network dynamics), various challenges arise from the specific application areas (such as privacy concerns in cyber-physical systems or the need for \emph{scalable} algorithm designs due to the large size of various biological and engineered networks). On the other hand, networked systems provide significant opportunities and allow for performance and robustness levels that are far beyond reach for centralized systems, with examples ranging from the Internet (of Things) to the smart grid and the brain. This dissertation aims to address several of these challenges and harness these opportunities. The dissertation is divided into three parts. In the first part, we study privacy concerns whose resolution is vital for the utility of networked cyber-physical systems. We study the problems of average consensus and convex optimization as two principal distributed computations occurring over networks and design algorithm with rigorous privacy guarantees that provide a \emph{best achievable} tradeoff between network utility and privacy. In the second part, we analyze networks with resource constraints. More specifically, we study three problems of stabilization under communication (bandwidth and latency) limitations in sensing and actuation, optimal time-varying control scheduling problem under limited number of actuators and control energy, and the structure identification problem of under-sensed networks (i.e., networks with latent nodes). Finally in the last part, we focus on the intersection of networked dynamical systems and neuroscience and draw connections between brain network dynamics and two extensively studied but yet not fully understood neuro-cognitive phenomena: goal-driven selective attention and neural oscillations. Using a novel axiomatic approach, we establish these connections in the form of necessary and/or sufficient conditions on the network structure that match the network output trajectories with experimentally observed brain activity