Abstract: The message-passing paradigm has served as the foundation of graph neural networks (GNNs) for years, making them achieve great success in a wide range of applications. Despite its elegance, ...

Abstract: Graph theory and machine learning are revolutionary approaches to medical image analysis that leverage the structural nuances of medical data for better diagnostic accuracy. This research ...

This repository contains the official implementation of our ICML 2024 paper, VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context. VisionGraph, is a benchmark ...

This project is a Transformer-based graph representation learning framework for pan-cancer gene identifications. It applies to both homogeneous networks (PPI network)and heterogeneous networks. TREE ...