logo_upc2.jpg                                                                   Complex networks and data analysis



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


Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation

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).


Unravelling the community structure of the climate system by using lags and symbolic time-series analysis

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).


Assessing the direction of climate interactions by means of complex networks and information theoretic tools

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)




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)








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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