arXiv subjects

Antonio Peruzzi

Publications and source records attributed to Antonio Peruzzi.

10 recordsLinked to original sources

arXiv2026

Generalized Poisson Dynamic Network Models

Count-weighted temporal networks often exhibit unequal dispersion in the edge weights, which cannot be fully explained by modelling observational heterogeneity through latent factors in the conditional mean. Therefore, we propose new dynamic network model classes exploiting the Generalized Poisson distribution to capture both under- and overdispersion. We consider three different dynamic specifications: latent factor dynamics, autoregressive dynamics, and latent position dynamics, and study some theoretical properties of the random networks, showing the impact of the dispersion parameter on the random network's connectivity. After discussing the parameter identification strategy, we present a Bayesian inference procedure along with a posterior sampling algorithm. A numerical illustration demonstrates the effectiveness of the designed algorithm and provides estimates of the misspecification bias when unequal dispersion is neglected. Our new models are then applied to two relevant dynamic datasets considered in previous studies: a set of bike-sharing dynamic networks and a set of dynamic media networks. Our results highlight the importance of explicitly modeling overdispersion for both an accurate in-sample fit and out-of-sample performance.

stat.ME↗

arXiv2026

A Bayesian Dynamic Latent Space Model for Weighted Networks

A new dynamic latent space eigenmodel (LSM) is proposed for weighted temporal networks. The model accommodates integer-valued weights, excess of zeros, time-varying node positions (features), and time-varying network sparsity. The latent positions evolve according to a vector autoregressive process that accounts for lagged and contemporaneous dependence across nodes and features, a characteristic neglected in the LSM literature. A Bayesian approach is used to address two of the primary sources of inference intractability in dynamic LSMs: latent feature estimation and the choice of latent space dimension. We employ an efficient auxiliary-mixture sampler that performs data augmentation and supports conditionally conjugate prior distributions. A point-process representation of the network weights and the finite-dimensional distribution of the latent processes are used to derive a multi-move sampler in which each feature trajectory is drawn in a single block, without recursions. This sampling strategy is new to the network literature and can significantly reduce computational time while improving chain mixing. To avoid trans-dimensional samplers, a Laplace approximation of the partial marginal likelihood is used to design a partially collapsed Gibbs sampler. Overall, our procedure is general, as it can be easily adapted to static and dynamic settings, as well as to other discrete or continuous weight distributions.

stat.ME↗

arXiv2025

Bayesian Outlier Detection for Matrix-variate Models

Anomalies in economic and financial data -- often linked to rare yet impactful events -- are of theoretical interest, but can also severely distort inference. Although outlier-robust methodologies can be used, many researchers prefer pre-processing strategies that remove outliers. In this work, an efficient sequential Bayesian framework is proposed for outlier detection based on the predictive Bayes Factor (BF). The proposed method is specifically designed for large, multidimensional datasets and extends univariate Bayesian model outlier detection procedures to the matrix-variate setting. Leveraging power-discounted priors, tractable predictive BF are obtained, thereby avoiding computationally intensive techniques. The BF finite sample distribution, the test critical region, and robust extensions of the test are introduced by exploiting the sampling variability. The framework supports online detection with analytical tractability, ensuring both accuracy and scalability. Its effectiveness is demonstrated through simulations, and three applications to reference datasets in macroeconomics and finance are provided.

stat.ME↗

arXiv2024

Comment on 'Sparse Bayesian Factor Analysis when the Number of Factors is Unknown' by S. Fr\"uhwirth-Schnatter, D. Hosszejni, and H. Freitas Lopes

The techniques suggested in Fr\"uhwirth-Schnatter et al. (2024) concern sparsity and factor selection and have enormous potential beyond standard factor analysis applications. We show how these techniques can be applied to Latent Space (LS) models for network data. These models suffer from well-known identification issues of the latent factors due to likelihood invariance to factor translation, reflection, and rotation (see Hoff et al., 2002). A set of observables can be instrumental in identifying the latent factors via auxiliary equations (see Liu et al., 2021). These, in turn, share many analogies with the equations used in factor modeling, and we argue that the factor loading restrictions may be beneficial for achieving identification.

stat.ME↗

arXiv2024

A Multiple Random Scan Strategy for Latent Space Models

Latent Space (LS) network models project the nodes of a network on a $d$-dimensional latent space to achieve dimensionality reduction of the network while preserving its relevant features. Inference is often carried out within a Markov Chain Monte Carlo (MCMC) framework. Nonetheless, it is well-known that the computational time for this set of models increases quadratically with the number of nodes. In this work, we build on the Random-Scan (RS) approach to propose an MCMC strategy that alleviates the computational burden for LS models while maintaining the benefits of a general-purpose technique. We call this novel strategy Multiple RS (MRS). This strategy is effective in reducing the computational cost by a factor without severe consequences on the MCMC draws. Moreover, we introduce a novel adaptation strategy that consists of a probabilistic update of the set of latent coordinates of each node. Our Adaptive MRS adapts the acceptance rate of the Metropolis step to adjust the probability of updating the latent coordinates. We show via simulation that the Adaptive MRS approach performs better than MRS in terms of mixing. Finally, we apply our algorithm to a multi-layer temporal LS model and show how our adaptive strategy may be beneficial to empirical applications.

stat.CO↗

arXiv2023

A Spatiotemporal Gamma Shot Noise Cox Process

A new discrete-time shot noise Cox process for spatiotemporal data is proposed. The random intensity is driven by a dependent sequence of latent gamma random measures. Some properties of the latent process are derived, such as an autoregressive representation and the Laplace functional. A simulation method based on the Inverse L\'evy Measure algorithm is provided. Moreover, these results are used to derive the moment, predictive, and pair correlation measures for the proposed shot noise Cox process. The model is flexible yet tractable, allowing it to capture persistence, global trends, and latent spatial and temporal factors. A Bayesian inference approach is adopted, and efficient Markov Chain Monte Carlo algorithms based on conditional Sequential Monte Carlo and adaptive Metropolis-Hastings are proposed. An application to georeferenced wildfire data illustrates the properties of the model and inference.

stat.ME↗

arXiv2023

Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model

News outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here, we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. We contribute to the literature in several directions: 1) Our model provides a novel measure of media bias that combines information from both network data and text-based indicators; 2) we endow our model with Markov-Switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered.

stat.AP↗

arXiv2023

Unveiling the Hidden Agenda: Biases in News Reporting and Consumption

One of the most pressing challenges in the digital media landscape is understanding the impact of biases on the news sources that people rely on for information. Biased news can have significant and far-reaching consequences, influencing our perspectives and shaping the decisions we make, potentially endangering the public and individual well-being. With the advent of the Internet and social media, discussions have moved online, making it easier to disseminate both accurate and inaccurate information. To combat mis- and dis-information, many have begun to evaluate the reliability of news sources, but these assessments often only examine the validity of the news (narrative bias) and neglect other types of biases, such as the deliberate selection of events to favor certain perspectives (selection bias). This paper aims to investigate these biases in various news sources and their correlation with third-party evaluations of reliability, engagement, and online audiences. Using machine learning to classify content, we build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. Our results show that the source classification provided by third-party organizations closely follows the narrative bias dimension, while it is much less accurate in identifying the selection bias. Moreover, we found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Lastly, analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.

cs.CY↗

arXiv2021

Lack of evidence for correlation between COVID-19 infodemic and vaccine acceptance

How information consumption affects behaviour is an open and widely debated research question. A popular hypothesis states that the so-called infodemic has a substantial impact on orienting individual decisions. A competing hypothesis stresses that exposure to vast amounts of even contradictory information has little effect on personal choices. The COVID-19 pandemic offered an opportunity to investigate this relationship, analysing the interplay between COVID-19 related information circulation and the propensity of users to get vaccinated. We analyse the vaccine infodemics on Twitter and Facebook by looking at 146M contents produced by 20M accounts between 1 January 2020 and 30 April 2021. We find that vaccine-related news triggered huge interest through social media, affecting attention patterns and the modality in which information was spreading. However, we observe that such a tumultuous information landscape translated only in minimal variations in overall vaccine acceptance as measured by Facebook's daily COVID-19 Trends and Impact Survey (previously known as COVID-19 World Symptoms Survey) on a sample of 1.6M users. Notably, the observation period includes the European Medicines Agency (EMA) investigations over blood clots cases potentially related to vaccinations, a series of events that could have eroded trust in vaccination campaigns. We conclude the paper by investigating the numerical correlation between various infodemics indices and vaccine acceptance, observing strong compatibility with a null model. This finding supports the hypothesis that altered information consumption patterns are not a reliable predictor of collective behavioural change. Instead, wider attention on social media seems to resolve in polarisation, with the vaccine-prone and the vaccine-hesitant maintaining their positions.

physics.soc-ph↗

arXiv2019

(Mis)Information Operations: An Integrated Perspective

The massive diffusion of social media fosters disintermediation and changes the way users are informed, the way they process reality, and the way they engage in public debate. The cognitive layer of users and the related social dynamics define the nature and the dimension of informational threats. Users show the tendency to interact with information adhering to their preferred narrative and to ignore dissenting information. Confirmation bias seems to account for users decisions about consuming and spreading content; and, at the same time, aggregation of favored information within those communities reinforces group polarization. In this work, the authors address the problem of (mis)information operations with a holistic and integrated approach. Cognitive weakness induced by this new information environment are considered. Moreover, (mis)information operations, with particular reference to the Italian context, are considered; and the fact that the phenomenon is more complex than expected is highlighted. The paper concludes by providing an integrated research roadmap accounting for the possible future technological developments.

cs.CY↗