Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...

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 ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...

Welcome to the Data Structures and Algorithms Repository! My aim for this project is to serve as a comprehensive collection of problems and solutions implemented in Python, aimed at mastering ...

Abstract: This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals ...

Hedera’s unique hashgraph architecture enables high speed, scalability and energy efficiency, distinguishing it from traditional blockchains. Hedera’s governance through a council of global ...