Publications

2025

1. Preprint

   Community Membership Hiding via Gradient-based Optimization

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

   2025

   Abs PDF Code

   We tackle the problem of community membership hiding, which involves strategically altering a network’s structure to obscure a target node’s membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose ∇-CMH, a gradient-based method that modifies the network’s structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency.

2024

1. Preprint

   Analysis of Hopfield Model as Associative Memory

   Matteo Silvestri

   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.