IEEE

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Abstract: Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series ...
IEEE

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Abstract: Graph neural networks (GNNs) have attracted extensive research attention in recent years due to their capability to progress with graph data and have been widely used in practical ...
GitHub

Graph Neural Prompting with Large Language Models

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in ...
GitHub

Knowledge graph-enhanced molecular contrastive learning with functional prompt

This repository is the official implementation of KANO, which is model proposed in a paper: Knowledge graph-enhanced molecular contrastive learning with functional prompt. Firstly, we construct a ...