Ming Jin

Time-LLM. [ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

Awesome-GNN4TS. [TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)

Weibo-Analyst. Social media (Weibo) comments analyzing toolbox in Chinese 微博评论分析工具, 实现功能: 1.微博评论数据爬取; 2.分词与关键词提取; 3.词云与词频统计; 4.情感分析; 5.主题聚类

Neural-Temporal-Walks. [NeurIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"

SL-GAD. [TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection".

TGN. A PyTorch annotated replication of the paper: https://arxiv.org/abs/2006.10637

TGGC. [TPAMI 2025] Official implementation of "Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting"

STGCN. A PyTorch implementation of the paper https://arxiv.org/abs/1709.04875

DCRNN. A simplified PyTorch implementation of the paper https://arxiv.org/abs/1707.01926

Awesome-TimeSeries-SpatioTemporal-LM-LLM. A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, Event Data, and AIOps.

Awesome-Deep-Graph-Anomaly-Detection. Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.

Awesome-Trustworthy-GNNs.

anchor. Code for "High-Precision Model-Agnostic Explanations" paper

GraphSage. A PyTorch implementation of the paper https://www-cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf

MTGODE. [TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs".