AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
IEEE

Graph Multi-Convolution and Attention Pooling for Graph Classification

Abstract: Many studies have achieved excellent performance in analyzing graph-structured data. However, learning graph-level representations for graph classification is still a challenging task.
IEEE

Integrating Fuzzy Clustering and Graph Convolution Network to Accurately Identify Clusters From Attributed Graph

Abstract: Attributed graph clustering is of significance for an in-depth understanding of the intrinsic organization of complex networks. Recently, owing to the powerful learning capability of deep ...
GitHub

DiffKG: Knowledge Graph Diffusion Model for Recommendation

This is the PyTorch implementation for DiffKG proposed in the paper DiffKG: Knowledge Graph Diffusion Model for Recommendation, which is accepted by WSDM 2024 Oral. Yangqin Jiang, Yuhao Yang, Lianghao ...
GitHub

DSTFGCN: A Dynamic Spatial-temporal Fusion Graph Convolution Network for Traffic Flow Forecasting

This repository contains the implementation of DSTFGCN, a deep learning model for traffic flow forecasting.
www.cs.cmu.edu

Tom Mitchell

What about ChatGPT and related large AI Systems? How will they impact us all? As a longtime researcher in AI, I'm excited about the ways in which these new AI systems can improve our healthcare, ...