graph neural networks

Dynamic coarsened spatio-temporal graph convolutional networks for fMRI classification of addiction-induced sleep disorders

Substance use disorder is frequently characterized by persistent sleep disorders that hinder cognitive recovery. Accurately identifying these disruptions requires methods capable of tracking both spatial interactions and the temporal evolution of brain activity. While functional magnetic resonance imaging (fMRI) offers...

Toward a Simulated Avian Visual System: Connectome Mapping of the Pigeon Tectofugal Pathway as a Biologically-Grounded CNN Alternative

Image recognition remains one of the central unsolved problems in machine learning. Despite superhuman benchmark accuracy, convolutional neural networks (CNNs) fail systematically at the tasks that define real-world deployment: they require orders of magnitude more labeled data than biological learners, collapse under...

Machine-Learning-Assisted Inverse Design of Biopharmaceutical Formulations and Delivery Systems

Pharmaceutical formulation and delivery-device development has historically proceeded by forward trial and error: a candidate material, formulation, or geometry is proposed, fabricated, and tested, and the cycle repeats until an acceptable product emerges. Machine learning enables an inverse alternative: given a target...

Replication Package for the Master's Thesis: Integrating Structural and Semantic Analysis for Code Smell Refactoring Prediction

This repository contains the official replication package, automation scripts, and machine learning pipelines developed for the Master's Thesis: "Integrating Structural and Semantic Analysis for Code Smell Refactoring Prediction" (Università degli Studi di Milano-Bicocca, 2026). The project bridges traditional static c...

Reproducibility Package for What Information Can Be Discarded

Release purpose This archive is the release-grade executable evidence and verification system for “What Information Can Be Discarded?” It freezes the proof-support artifacts, experiment suites, source code, public or generated inputs, protocols, environment locks, sealed results, statistical analyses, and paper-to-resu...

Replication Package for the Master's Thesis: Integrating Structural and Semantic Analysis for Code Smell Refactoring Prediction

This repository contains the official replication package, automation scripts, and machine learning pipelines developed for the Master's Thesis: "Integrating Structural and Semantic Analysis for Code Smell Refactoring Prediction" (Università degli Studi di Milano-Bicocca, 2026). The project bridges traditional static c...

Geometry-aware liquid-graph neural network for surface-pressure fields reconstruction and extrapolation on high-speed magnetic levitation train under diverse wind conditions

A two-stage hybrid calibration framework for water distribution network models using Graph Neural Network

Adversarially Robust and Explainable Graph Neural Networks for Fraud Detection in Dynamic Blockchain Networks: A Systematic Solution-Based Literature Review

Blockchain technology has revolutionized digital financial systems through decentralized, transparent, and secure transactions. However, the increasing sophistication of blockchain systems has coincided with the emergence of complex illicit activities, including ransomware transactions, Ponzi schemes, phishing, and mon...

Solve the Useless Link Problem in Graph Condensation by Mining Link Information from Single Nodes

Countering silences in the EHRI collection: a graph neural network approach for archival justice

Abstract Searching for archival voids is a central concept in critical archival research, yet traditional approaches remain largely restricted by what is present and legible: Natural Language Processing and text-based methods tend to operate at the level of the document, struggling to engage with what is structurally a...

Graph neural networks for antarctic sea ice concentration forecasting

Antarctic sea ice is a crucial component of the climate system, yet in recent years it has undergone abrupt, poorly understood changes that motivate more accurate weeks-ahead forecasts. Existing statistical and dynamical approaches can struggle to represent the strongly nonlinear, heterogeneous, and rapidly evolving na...