Publications

2025

1. Article

   Exploring the potential of artificial intelligence in assessing the risk of gastric neoplastic lesions in patients with corpus atrophic gastritis

   Emanuele Dilaghi, Edoardo Cesaroni, Irene Ligato, and 6 more authors

   Gastric Cancer, Oct 2025

   Abs PDF

   Corpus atrophic gastritis (CAG) requires endoscopic-histological surveillance due to the risk of developing gastric neoplastic lesions (GNL). This study aimed to identify variables associated with GNL development at long-term follow-up using a Fisher score-based feature-ranking-approach coupled with a One-Class Support-Vector-Machine (SVM) model.

2. Preprint

   Evading Overlapping Community Detection via Proxy Node Injection

   Dario Loi, Matteo Silvestri, Fabrizio Silvestri, and 1 more author

   Sep 2025

   Abs PDF Code

   Protecting privacy in social graphs requires preventing sensitive information, such as community affiliations, from being inferred by graph analysis, without substantially altering the graph topology. We address this through the problem of \emphcommunity membership hiding (CMH), which seeks edge modifications that cause a target node to exit its original community, regardless of the detection algorithm employed. Prior work has focused on non-overlapping community detection, where trivial strategies often suffice, but real-world graphs are better modeled by overlapping communities, where such strategies fail. To the best of our knowledge, we are the first to formalize and address CMH in this setting. In this work, we propose a deep reinforcement learning (DRL) approach that learns effective modification policies, including the use of proxy nodes, while preserving graph structure. Experiments on real-world datasets show that our method significantly outperforms existing baselines in both effectiveness and efficiency, offering a principled tool for privacy-preserving graph modification with overlapping communities.

3. Preprint

   Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation

   Flavio Giorgi, Matteo Silvestri, Cesare Campagnano, and 2 more authors

   Sep 2025

   Abs PDF Code

   Explainable Artificial Intelligence has become a crucial area of research, aiming to demystify the decision-making processes of deep learning models. Among various explainability techniques, counterfactual explanations have been proven particularly promising, as they offer insights into model behavior by highlighting minimal changes that would alter a prediction. Despite their potential, these explanations are often complex and technical, making them difficult for non-experts to interpret. To address this challenge, we propose a novel pipeline that leverages Language Models, large and small, to compose narratives for counterfactual explanations. We employ knowledge distillation techniques along with a refining mechanism to enable Small Language Models to perform comparably to their larger counterparts while maintaining robust reasoning abilities. In addition, we introduce a simple but effective evaluation method to assess natural language narratives, designed to verify whether the models’ responses are in line with the factual, counterfactual ground truth. As a result, our proposed pipeline enhances both the reasoning capabilities and practical performance of student models, making them more suitable for real-world use cases.

4. Preprint

   The Right to Hide: Masking Community Affiliation via Minimal Graph Rewiring

   Matteo Silvestri, Edoardo Gabrielli, Fabrizio Silvestri, and 1 more author

   Aug 2025

   Abs PDF Code

   Protecting privacy in social graphs may require obscuring nodes’ membership in sensitive communities. However, doing so without significantly disrupting the underlying graph topology remains a key challenge. In this work, we address the community membership hiding problem, which involves strategically modifying the graph structure to conceal a target node’s affiliation with a community, regardless of the detection algorithm used. We reformulate the original discrete, counterfactual graph search objective as a differentiable constrained optimisation task. To this end, we introduce ∇-CMH, a new gradient-based method that operates within a feasible modification budget to minimise structural changes while effectively hiding a node’s community membership. Extensive experiments on multiple datasets and community detection methods demonstrate that our technique outperforms existing baselines, achieving the best balance between node hiding effectiveness and graph rewiring cost, while preserving computational efficiency.

2024

1. Preprint

   Analysis of Hopfield Model as Associative Memory

   Matteo Silvestri

   Feb 2024

   Abs PDF Code

   This article delves into the Hopfield neural network model, drawing inspiration from biological neural systems. The exploration begins with an overview of the model’s foundations, incorporating insights from mechanical statistics to deepen our understanding. Focusing on audio retrieval, the study demonstrates the Hopfield model’s associative memory capabilities. Through practical implementation, the network is trained to retrieve different patterns.