Price prediction for Crypto, Stock, and Index using Hybrid Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) models on PyTorch.

It is a comprehensive resource hub compiling all graph papers accepted at the International Conference on Learning Representations (ICLR) in 2024.

A framework to empover LLMs on graph reasoning and generation. Refer to our paper: https://arxiv.org/pdf/2402.08785.pdf

Code for our SIGKDD'22 paper Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.

Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.

A collection of AWESOME things about Graph-Related LLMs.

Extrapolating knowledge graphs from unstructured text using GPT-3 🕵️‍♂️

Time-series forecasting with GNNs

Transformer based model for time series prediction

Code and Content for Manning Publication on Graph Neural Networks

Scalable Graph Neural Networks with Deep Graph Library

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Stimulus-Response Network Analysis

A PyTorch implementation of the Attention Diffusion Network from "Structured Time Series Prediction without Structural Prior"