openalexZenodo (CERN European Organization for Nuclear Research)Cited by 0
Macro-Micro Cross-Attention Graph Neural Network for Molecular Reconstruction of Petroleum Fractions
Also available via: European Organization for Nuclear Research
Also available via: European Organization for Nuclear Research
In this work, we present a general Graph Neural Network (GNN) framework for learning algebraic properties of finite groups from their Cayley graph representations. The framework provides a unified computational pipeline consisting of a common graph construction procedure, feature…
Federica Benassi, A S Wadalkar, Lorenzo Mentaschi
This repository contains the data used for training and validation of the model presented in Decadal wave reconstruction in the Mediterranean Sea with graph neural networks by Benassi et al. The wave data will be published as Wadalkar et al. (2026), a bias-corrected version of th…
Jahnavi Somaraju, V. Guru Thrinath, S. R. Bhavishya, S. Bhavya, Y. Jahnavi
The rapid growth of online career and learning resources has made it difficult for job seekers and professionals to identify the skills, roles, and learning paths that best match their goals. This paper presents SkillGraph, a multi-agent architecture for AIpowered career recommen…
Research article: Shadow Banking Detection with Graph Neural Networks: Mapping Unofficial Lending Networks
The increasing reliance on third-party packages from repositories such as Python Package Index (PyPI) and Node Package Manager (NPM) has introduced critical vulnerabilities in software supply chains. Traditional security approaches, including signature-based detection and trust e…
Neural networks are extraordinarily effective and almost entirely opaque: their competence is real but unreadable, and adapting them means retraining usually via a data-center process, not something that happens in the moment, in context. Symbolic systems are the inverse, legible…