22,125 research outputs found
Uncovering the spatial structure of mobility networks
The extraction of a clear and simple footprint of the structure of large,
weighted and directed networks is a general problem that has many applications.
An important example is given by origin-destination matrices which contain the
complete information on commuting flows, but are difficult to analyze and
compare. We propose here a versatile method which extracts a coarse-grained
signature of mobility networks, under the form of a matrix that
separates the flows into four categories. We apply this method to
origin-destination matrices extracted from mobile phone data recorded in
thirty-one Spanish cities. We show that these cities essentially differ by
their proportion of two types of flows: integrated (between residential and
employment hotspots) and random flows, whose importance increases with city
size. Finally the method allows to determine categories of networks, and in the
mobility case to classify cities according to their commuting structure.Comment: 10 pages, 5 figures +Supplementary informatio
Sequences of purchases in credit card data reveal life styles in urban populations
Zipf-like distributions characterize a wide set of phenomena in physics,
biology, economics and social sciences. In human activities, Zipf-laws describe
for example the frequency of words appearance in a text or the purchases types
in shopping patterns. In the latter, the uneven distribution of transaction
types is bound with the temporal sequences of purchases of individual choices.
In this work, we define a framework using a text compression technique on the
sequences of credit card purchases to detect ubiquitous patterns of collective
behavior. Clustering the consumers by their similarity in purchases sequences,
we detect five consumer groups. Remarkably, post checking, individuals in each
group are also similar in their age, total expenditure, gender, and the
diversity of their social and mobility networks extracted by their mobile phone
records. By properly deconstructing transaction data with Zipf-like
distributions, this method uncovers sets of significant sequences that reveal
insights on collective human behavior.Comment: 30 pages, 26 figure
Causal Inference in Disease Spread across a Heterogeneous Social System
Diffusion processes are governed by external triggers and internal dynamics
in complex systems. Timely and cost-effective control of infectious disease
spread critically relies on uncovering the underlying diffusion mechanisms,
which is challenging due to invisible causality between events and their
time-evolving intensity. We infer causal relationships between infections and
quantify the reflexivity of a meta-population, the level of feedback on event
occurrences by its internal dynamics (likelihood of a regional outbreak
triggered by previous cases). These are enabled by our new proposed model, the
Latent Influence Point Process (LIPP) which models disease spread by
incorporating macro-level internal dynamics of meta-populations based on human
mobility. We analyse 15-year dengue cases in Queensland, Australia. From our
causal inference, outbreaks are more likely driven by statewide global
diffusion over time, leading to complex behavior of disease spread. In terms of
reflexivity, precursory growth and symmetric decline in populous regions is
attributed to slow but persistent feedback on preceding outbreaks via
inter-group dynamics, while abrupt growth but sharp decline in peripheral areas
is led by rapid but inconstant feedback via intra-group dynamics. Our proposed
model reveals probabilistic causal relationships between discrete events based
on intra- and inter-group dynamics and also covers direct and indirect
diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635
Mapping road network communities for guiding disease surveillance and control strategies
Human mobility is increasing in its volume, speed and reach, leading to the
movement and introduction of pathogens through infected travelers. An
understanding of how areas are connected, the strength of these connections and
how this translates into disease spread is valuable for planning surveillance
and designing control and elimination strategies. While analyses have been
undertaken to identify and map connectivity in global air, shipping and
migration networks, such analyses have yet to be undertaken on the road
networks that carry the vast majority of travellers in low and middle income
settings. Here we present methods for identifying road connectivity
communities, as well as mapping bridge areas between communities and key
linkage routes. We apply these to Africa, and show how many highly-connected
communities straddle national borders and when integrating malaria prevalence
and population data as an example, the communities change, highlighting regions
most strongly connected to areas of high burden. The approaches and results
presented provide a flexible tool for supporting the design of disease
surveillance and control strategies through mapping areas of high connectivity
that form coherent units of intervention and key link routes between
communities for targeting surveillance.Comment: 11 pages, 5 figures, research pape
A stochastic model of randomly accelerated walkers for human mobility
The recent availability of large databases allows to study macroscopic
properties of many complex systems. However, inferring a model from a fit of
empirical data without any knowledge of the dynamics might lead to erroneous
interpretations [6]. We illustrate this in the case of human mobility [1-3] and
foraging human patterns [4] where empirical long-tailed distributions of jump
sizes have been associated to scale-free super-diffusive random walks called
L\'evy flights [5]. Here, we introduce a new class of accelerated random walks
where the velocity changes due to acceleration kicks at random times, which
combined with a peaked distribution of travel times [7], displays a jump length
distribution that could easily be misinterpreted as a truncated power law, but
that is not governed by large fluctuations. This stochastic model allows us to
explain empirical observations about the movements of 780,000 private vehicles
in Italy, and more generally, to get a deeper quantitative understanding of
human mobility.Comment: 10 pages, 6 figures + Supplementary informatio
Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history
Spatial spread of infectious diseases among populations via the mobility of
humans is highly stochastic and heterogeneous. Accurate forecast/mining of the
spread process is often hard to be achieved by using statistical or mechanical
models. Here we propose a new reverse problem, which aims to identify the
stochastically spatial spread process itself from observable information
regarding the arrival history of infectious cases in each subpopulation. We
solved the problem by developing an efficient optimization algorithm based on
dynamical programming, which comprises three procedures: i, anatomizing the
whole spread process among all subpopulations into disjoint componential
patches; ii, inferring the most probable invasion pathways underlying each
patch via maximum likelihood estimation; iii, recovering the whole process by
assembling the invasion pathways in each patch iteratively, without burdens in
parameter calibrations and computer simulations. Based on the entropy theory,
we introduced an identifiability measure to assess the difficulty level that an
invasion pathway can be identified. Results on both artificial and empirical
metapopulation networks show the robust performance in identifying actual
invasion pathways driving pandemic spread.Comment: 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetic
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
Tracking Topology Dynamicity for Link Prediction in Intermittently Connected Wireless Networks
Through several studies, it has been highlighted that mobility patterns in
mobile networks are driven by human behaviors. This effect has been
particularly observed in intermittently connected networks like DTN (Delay
Tolerant Networks). Given that common social intentions generate similar human
behavior, it is relevant to exploit this knowledge in the network protocols
design, e.g. to identify the closeness degree between two nodes. In this paper,
we propose a temporal link prediction technique for DTN which quantifies the
behavior similarity between each pair of nodes and makes use of it to predict
future links. We attest that the tensor-based technique is effective for
temporal link prediction applied to the intermittently connected networks. The
validity of this method is proved when the prediction is made in a distributed
way (i.e. with local information) and its performance is compared to well-known
link prediction metrics proposed in the literature.Comment: Published in the proceedings of the 8th International Wireless
Communications and Mobile Computing Conference (IWCMC), Limassol, Cyprus,
201
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