Complex networks
and data analysis
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Many real-world systems,
constituted by nonlinear interacting units, are modelled by complex networks (or
graphs) in which the nodes represent the units and the links represent the
interactions among them. These interactions are often only partially known,
and we are interested in developing appropriated tools for inferring the
network structure, from observed data. Typically, only limited (and noisy)
data is available. We are also interested in extracting additional
information about underlying phenomena, from observed data (for the
identification of regime transitions, for uncovering the community structure
of highly inter-related nodes, etc.). We apply advanced data analysis
tools to two types of empirical data: -climatological time-series: we use
the network approach and various data analysis tools (ordinal analysis,
Hilbert analysis) to investigate long-range climatic interactions (inferring
the underlying “climate network”) and signatures of regime transitions that
can be related to “climate change”. - biomedical
images: we use symbolic analysis (2D extension of ordinal analysis) and the
network approach (by mapping a 2D image into a graph) to develop novel tools
for image classification. We analyze ocular images with the goal of providing
new measures for automatic/unsupervised classification. We also aim at
providing reliable early-warning indicators of ocular diseases such as
glaucoma. Recent Publications: Quantifying interdecadal
changes in large-scale patterns of surface air temperature variability D. A. Zappala,
M. Barreiro, and C. Masoller Earth System Dynamics 9,
383 (2018). Supplementary
information G. Tirabassi,
L. Sommerlade, and C. Masoller,
Chaos 27, 035815 (2017). Arxiv Quantification of
networks structural dissimilarities T. A. Schieber,
L. Carpi, A. Diaz-Guilera, P. M. Pardalos, C. Masoller, and M.
G. Ravetti, Nat. Comm. 8, 13928 (2017). Identifying
large-scale patterns of unpredictability and response to insolation in
atmospheric data F. Arizmendi,
M. Barreiro, C. Masoller, Sci. Rep. 7, 45676 (2017). Global atmospheric
dynamics investigated by using Hilbert frequency analysis D. A. Zappalà,
M. Barreiro, and C. Masoller, Entropy 18, 408 (2016). G. Tirabassi and C. Masoller, Scientific Reports 6, 29804 (2016). Inferring
the connectivity of coupled oscillators from time-series statistical
similarity analysis G. Tirabassi, R. Sevilla-Escoboza, J. M. Buldú
and C. Masoller, Scientific Reports 5 10829 (2015). J. I. Deza,
M. Barreiro and C. Masoller, Chaos 25, 033105
(2015). Video Quantifying
sudden changes in dynamical systems using symbolic networks C. Masoller,
Y. Hong, S. Ayad, F. Gustave, S. Barland, A. J. Pons, S. Gómez and A. Arenas, New Journal
of Physics 17, 023068 (2015). A study of the air-sea
interaction in the South Atlantic Convergence Zone through Granger Causality G. Tirabassi,
C. Masoller and M. Barreiro,International Journal of Climatology 35, 3440
(2015). PhD Thesis Ignacio Deza: Climate networks
constructed by using information-theoretic measures and ordinal time-series
analysis (February 2015) Giulio Tirabassi: Disentangling
climate interactions and inferring tipping points by using complex networks
(June 2015) Funding ITN BEOPTICAL (H2020-675512). Read more about
this project. ITN LINC (FP7-289447). Read more about this project. Read
more about the results. Complex physical and biophysical systems (ComPhysBio,
FIS2015-66503-C3-2-P) ICREA ACADEMIA
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Click on the
image to download the paper
Quantifying changes in surface air
temperature dynamics during the last 30 years (Earth System Dynamics 2018).
Hilbert analysis of surface air
temperature. Watch the video of
how seasons evolve during a normal year,
a El Niño year
and a La Niña
year (temporal evolution of the cosine of the Hilbert phase)
Quantifying network dissimilarities
(Nat. Comm. 2017)
Identying regions with nonlinear
surface air temperature response to solar forcing (Sci. Rep. 2017) |
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