533 research outputs found
Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach
The increasing availability of temporal network data is calling for more
research on extracting and characterizing mesoscopic structures in temporal
networks and on relating such structure to specific functions or properties of
the system. An outstanding challenge is the extension of the results achieved
for static networks to time-varying networks, where the topological structure
of the system and the temporal activity patterns of its components are
intertwined. Here we investigate the use of a latent factor decomposition
technique, non-negative tensor factorization, to extract the community-activity
structure of temporal networks. The method is intrinsically temporal and allows
to simultaneously identify communities and to track their activity over time.
We represent the time-varying adjacency matrix of a temporal network as a
three-way tensor and approximate this tensor as a sum of terms that can be
interpreted as communities of nodes with an associated activity time series. We
summarize known computational techniques for tensor decomposition and discuss
some quality metrics that can be used to tune the complexity of the factorized
representation. We subsequently apply tensor factorization to a temporal
network for which a ground truth is available for both the community structure
and the temporal activity patterns. The data we use describe the social
interactions of students in a school, the associations between students and
school classes, and the spatio-temporal trajectories of students over time. We
show that non-negative tensor factorization is capable of recovering the class
structure with high accuracy. In particular, the extracted tensor components
can be validated either as known school classes, or in terms of correlated
activity patterns, i.e., of spatial and temporal coincidences that are
determined by the known school activity schedule
Mitigation of infectious disease at school: targeted class closure vs school closure
School environments are thought to play an important role in the community
spread of airborne infections (e.g., influenza) because of the high mixing
rates of school children. The closure of schools has therefore been proposed as
efficient mitigation strategy, with however high social and economic costs:
alternative, less disruptive interventions are highly desirable. The recent
availability of high-resolution contact networks in school environments
provides an opportunity to design micro-interventions and compare the outcomes
of alternative mitigation measures. We consider mitigation measures that
involve the targeted closure of school classes or grades based on readily
available information such as the number of symptomatic infectious children in
a class. We focus on the case of a primary school for which we have
high-resolution data on the close-range interactions of children and teachers.
We simulate the spread of an influenza-like illness in this population by using
an SEIR model with asymptomatics and compare the outcomes of different
mitigation strategies. We find that targeted class closure affords strong
mitigation effects: closing a class for a fixed period of time -equal to the
sum of the average infectious and latent durations- whenever two infectious
individuals are detected in that class decreases the attack rate by almost 70%
and strongly decreases the probability of a severe outbreak. The closure of all
classes of the same grade mitigates the spread almost as much as closing the
whole school. Targeted class closure strategies based on readily available
information on symptomatic subjects and on limited information on mixing
patterns, such as the grade structure of the school, can be almost as effective
as whole-school closure, at a much lower cost. This may inform public health
policies for the management and mitigation of influenza-like outbreaks in the
community
Activity clocks: spreading dynamics on temporal networks of human contact
Dynamical processes on time-varying complex networks are key to understanding
and modeling a broad variety of processes in socio-technical systems. Here we
focus on empirical temporal networks of human proximity and we aim at
understanding the factors that, in simulation, shape the arrival time
distribution of simple spreading processes. Abandoning the notion of wall-clock
time in favour of node-specific clocks based on activity exposes robust
statistical patterns in the arrival times across different social contexts.
Using randomization strategies and generative models constrained by data, we
show that these patterns can be understood in terms of heterogeneous
inter-event time distributions coupled with heterogeneous numbers of events per
edge. We also show, both empirically and by using a synthetic dataset, that
significant deviations from the above behavior can be caused by the presence of
edge classes with strong activity correlations
Estimating the outcome of spreading processes on networks with incomplete information: a mesoscale approach
Recent advances in data collection have facilitated the access to
time-resolved human proximity data that can conveniently be represented as
temporal networks of contacts between individuals. While this type of data is
fundamental to investigate how information or diseases propagate in a
population, it often suffers from incompleteness, which possibly leads to
biased conclusions. A major challenge is thus to estimate the outcome of
spreading processes occurring on temporal networks built from partial
information. To cope with this problem, we devise an approach based on
Non-negative Tensor Factorization (NTF) -- a dimensionality reduction technique
from multi-linear algebra. The key idea is to learn a low-dimensional
representation of the temporal network built from partial information, to adapt
it to take into account temporal and structural heterogeneity properties known
to be crucial for spreading processes occurring on networks, and to construct
in this way a surrogate network similar to the complete original network. To
test our method, we consider several human-proximity networks, on which we
simulate a loss of data. Using our approach on the resulting partial networks,
we build a surrogate version of the complete network for each. We then compare
the outcome of a spreading process on the complete networks (non altered by a
loss of data) and on the surrogate networks. We observe that the epidemic sizes
obtained using the surrogate networks are in good agreement with those measured
on the complete networks. Finally, we propose an extension of our framework
when additional data sources are available to cope with the missing data
problem
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
Predicting human mobility through the assimilation of social media traces into mobility models
Predicting human mobility flows at different spatial scales is challenged by
the heterogeneity of individual trajectories and the multi-scale nature of
transportation networks. As vast amounts of digital traces of human behaviour
become available, an opportunity arises to improve mobility models by
integrating into them proxy data on mobility collected by a variety of digital
platforms and location-aware services. Here we propose a hybrid model of human
mobility that integrates a large-scale publicly available dataset from a
popular photo-sharing system with the classical gravity model, under a stacked
regression procedure. We validate the performance and generalizability of our
approach using two ground-truth datasets on air travel and daily commuting in
the United States: using two different cross-validation schemes we show that
the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure
Gender homophily from spatial behavior in a primary school: a sociometric study
We investigate gender homophily in the spatial proximity of children (6 to 12
years old) in a French primary school, using time-resolved data on face-to-face
proximity recorded by means of wearable sensors. For strong ties, i.e., for
pairs of children who interact more than a defined threshold, we find
statistical evidence of gender preference that increases with grade. For weak
ties, conversely, gender homophily is negatively correlated with grade for
girls, and positively correlated with grade for boys. This different evolution
with grade of weak and strong ties exposes a contrasted picture of gender
homophily
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