arXiv subjects

Francesca Santucci

Publications and source records attributed to Francesca Santucci.

4 recordsLinked to original sources

arXiv2026

Missing links prediction: comparing machine learning with physics-rooted approaches

An active research line within the broader field of network science is the one concerning link prediction. Close in scope to network reconstruction, link prediction targets specific connections with the aim of uncovering the missing ones, as well as predicting those most likely to emerge in the future, from the available information. In this paper, we consider two families of methods, i.e. those rooted in statistical physics and those based upon machine learning: the members of the first family identify missing links as the most probable non-observed ones, the probability coefficients being determined by solving maximum-entropy benchmarks over the accessible network structure; the members of the second family, instead, associate the presence of single edges to explanatory node-specific variables. Running likelihood-based models such as the Configuration Model, or one of its many fitness-based variants, in parallel with the Gradient Boosting Decision Tree algorithm reveals that the accuracy of the former is comparable to the accuracy of the latter. Such a result confirms that white-box algorithms are viable competitors to the currently available black-box ones, being more interpretable and computationally faster.

physics.soc-ph↗

arXiv2023

Noise-cleaning the precision matrix of fMRI time series

We present a comparison between various algorithms of inference of covariance and precision matrices in small datasets of real vectors, of the typical length and dimension of human brain activity time series retrieved by functional Magnetic Resonance Imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists in denoising the empirically observed matrices in order to obtain a better estimator of the true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of datasets. The first type are time series of fMRI brain activity of human subjects at rest. The second type are synthetic time series sampled from a generative Gaussian model of which we can vary the fraction of dimensions per sample q = N/T and the strength of off-diagonal correlations. The reliability of each algorithm is assessed in terms of test-set likelihood and, in the case of synthetic data, of the distance from the true precision matrix. We observe that the so called Optimal Rotationally Invariant Estimator, based on Random Matrix Theory, leads to a significantly lower distance from the true precision matrix in synthetic data, and higher test likelihood in natural fMRI data. We propose a variant of the Optimal Rotationally Invariant Estimator in which one of its parameters is optimised by cross-validation. In the severe undersampling regime (large q) typical of fMRI series, it outperforms all the other estimators. We furthermore propose a simple algorithm based on an iterative likelihood gradient ascent, providing an accurate estimation for weakly correlated datasets.

cond-mat.stat-mech↗

arXiv2022

Laplacian paths in complex networks: information core emerges from entropic transitions

Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales reflecting structural insights that are not manifest from analyses of the network topology. Moreover, small-world effects correlate with the different network hierarchies complicating the identification of coexisting mesoscopic structures and functional cores. We present a communicability analysis of effective information pathways throughout complex networks based on information diffusion to shed further light on these issues. We employ a variety of brand-new theoretical techniques allowing for: (i) bring the theoretical framework to quantify the probability of information diffusion among nodes, (ii) identify critical scales and structures of complex networks regardless of their intrinsic properties, and (iii) demonstrate their dynamical relevance in synchronization phenomena. By combining these ideas, we evidence how the information flow on complex networks unravels different resolution scales. Using computational techniques, we focus on entropic transitions, uncovering a generic mesoscale object, the information core, and controlling information processing in complex networks. Altogether, this study sheds much light on allowing new theoretical techniques paving the way to introduce future renormalization group approaches based on diffusion distances.

physics.soc-ph↗

arXiv2021

An immersive Open Source environment using Godot

We present a sample implementation of a Virtual and Augmented Reality immersive environment based on Free and Libre Open Source Hardware and Software and the HTC Vive system, used to enhance the immersive experience of the user and to track her/his movements. The sense of immersion has increased and stimulated using a footplate and a Tibetan bridge, connected to the virtual world as Augmented Reality applications and implemented through an Arduino board, thereby adopting a low cost, open source hardware and software approach. The proposed architecture is relatively affordable from the cost point of view, easy to implement, configure and adapt to different contexts. It can be of great help for organizing laboratory classes for young students to afford the implementation of virtual worlds and Augmented Reality applications.

cs.GR↗