This repository contains a refactoring of the code used in the paper "Learning Latent Graph Structures and Their Uncertainty" (ICML 2025). The code is designed to be modular and easy to use, allowing ...
Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...
Abstract: Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental ...
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