32,991 research outputs found
Understanding the Impact of Network Structure on Propagation Dynamics based on Mobile Big Data
Understanding the propagation dynamics of information and epidemic on complex networks is very important for discovering and controlling a terrorist attack, and even for predicting a disease outbreak. How to track, recognize and model such dynamics is a big challenge. Along with the popularity of smart devices and the rapid development of Internet of Things (IoT), massive mobile data is automatically collected. In this article, as a typical use case, we investigate the impact of network structure on epidemic propagation dynamics by analyzing the mobile data collected from smart devices carried by the volunteers of Ebola outbreak areas. From this investigation, we obtain two observations. Based on these observations and the analytical ability of Apache Spark on Streaming Data and Graph, we propose a simple model to recognize the dynamic structure of a network. Moreover, we introduce and discuss open issues and future work for developing the proposed recognition model
Flooding through the lens of mobile phone activity
Natural disasters affect hundreds of millions of people worldwide every year.
Emergency response efforts depend upon the availability of timely information,
such as information concerning the movements of affected populations. The
analysis of aggregated and anonymized Call Detail Records (CDR) captured from
the mobile phone infrastructure provides new possibilities to characterize
human behavior during critical events. In this work, we investigate the
viability of using CDR data combined with other sources of information to
characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map
has been reconstructed using Landsat-7 images to identify the floods. Within
this frame, the underlying communication activity signals in the CDR data have
been analyzed and compared against rainfall levels extracted from data of the
NASA-TRMM project. The variations in the number of active phones connected to
each cell tower reveal abnormal activity patterns in the most affected
locations during and after the floods that could be used as signatures of the
floods - both in terms of infrastructure impact assessment and population
information awareness. The representativeness of the analysis has been assessed
using census data and civil protection records. While a more extensive
validation is required, these early results suggest high potential in using
cell tower activity information to improve early warning and emergency
management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC)
201
Temporal similarity metrics for latent network reconstruction: The role of time-lag decay
When investigating the spreading of a piece of information or the diffusion
of an innovation, we often lack information on the underlying propagation
network. Reconstructing the hidden propagation paths based on the observed
diffusion process is a challenging problem which has recently attracted
attention from diverse research fields. To address this reconstruction problem,
based on static similarity metrics commonly used in the link prediction
literature, we introduce new node-node temporal similarity metrics. The new
metrics take as input the time-series of multiple independent spreading
processes, based on the hypothesis that two nodes are more likely to be
connected if they were often infected at similar points in time. This
hypothesis is implemented by introducing a time-lag function which penalizes
distant infection times. We find that the choice of this time-lag strongly
affects the metrics' reconstruction accuracy, depending on the network's
clustering coefficient and we provide an extensive comparative analysis of
static and temporal similarity metrics for network reconstruction. Our findings
shed new light on the notion of similarity between pairs of nodes in complex
networks
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
Hybrid Epidemics - A Case Study on Computer Worm Conficker
Conficker is a computer worm that erupted on the Internet in 2008. It is
unique in combining three different spreading strategies: local probing,
neighbourhood probing, and global probing. We propose a mathematical model that
combines three modes of spreading, local, neighbourhood and global to capture
the worm's spreading behaviour. The parameters of the model are inferred
directly from network data obtained during the first day of the Conifcker
epidemic. The model is then used to explore the trade-off between spreading
modes in determining the worm's effectiveness. Our results show that the
Conficker epidemic is an example of a critically hybrid epidemic, in which the
different modes of spreading in isolation do not lead to successful epidemics.
Such hybrid spreading strategies may be used beneficially to provide the most
effective strategies for promulgating information across a large population.
When used maliciously, however, they can present a dangerous challenge to
current internet security protocols
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