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:


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. in press (2017).


Global atmospheric dynamics investigated by using Hilbert frequency analysis

Dario A. Zappalà, Marcelo Barreiro, and Cristina 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).


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)


ITN LINC (FP7-289447). LINC results were published in Cordis (main portal for EU-funded research projects), read more here.


Complex physical and biophysical systems (ComPhysBio, FIS2015-66503-C3-2-P)









From top to bottom: quantifying structural differences between networks (Nat. Comm. 2017), assesing the directionality of climate interactions (Chaos 2015), inferring climate comunities (Sci. Rep. 2016) and identying regions with nonlinear surface air temperature response to solar forcing (Sci. Rep. 2017). Click in the image to download the paper or watch the video.


Back to Cristina Masoller’s web page