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KimMeen avatar

KimMeen/Awesome-GNN4TS

0
View on GitHub↗
861 stars·75 forks·11 viewsarxiv.org/abs/2307.03759↗

Awesome GNN4TS

Awesome Graph Neural Networks for Time Series Analysis (GNN4TS)

Features

  • Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
  • Research and Surveys - Survey of graph neural networks for time series tasks.
  • Research Surveys and Tutorials - Survey on graph neural networks for forecasting, classification, and anomaly detection.

Star history

Star history chart for kimmeen/awesome-gnn4tsStar history chart for kimmeen/awesome-gnn4ts

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Awesome GNN4TS

These projects share indexed features with Awesome GNN4TS. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • qingsongedu/awesome-ssl4tsqingsongedu avatar

    qingsongedu/Awesome-SSL4TS

    381View on GitHub↗

    A professionally curated list of awesome resources (paper, code, data, etc.) on Self-Supervised Learning for Time Series (SSL4TS), which is the first work to comprehensively and systematically summarize the recent advances of Self-Supervised Learning for modeling time series data to the best of…

    View on GitHub↗381
  • xiaoxinhe/awesome-graph-llmXiaoxinHe avatar

    XiaoxinHe/Awesome-Graph-LLM

    2,439View on GitHub↗

    A collection of AWESOME things about Graph-Related Large Language Models (LLMs).

    View on GitHub↗2,439
  • hannibal046/awesome-llmHannibal046 avatar

    Hannibal046/Awesome-LLM

    26,933View on GitHub↗

    This project serves as a comprehensive, static directory of external resources dedicated to the study and application of large language models. It functions as a centralized discovery point for developers and researchers, aggregating foundational academic papers, technical documentation, and specialized tools within a structured, version-controlled knowledge base. The repository distinguishes itself through a multi-level classification system that organizes diverse technical domains, ranging from model training frameworks and inference optimization to AI safety and hallucination detection. By

    View on GitHub↗26,933
  • bradyfu/awesome-multimodal-large-language-modelsBradyFU avatar

    BradyFU/Awesome-Multimodal-Large-Language-Models

    17,892View on GitHub↗

    :sparkles::sparkles:Latest Advances on Multimodal Large Language Models

    chain-of-thoughtin-context-learninginstruction-following
    View on GitHub↗17,892
Compare all 30 related projects→

Frequently asked questions

What does kimmeen/awesome-gnn4ts do?

Awesome Graph Neural Networks for Time Series Analysis (GNN4TS)

What are the main features of kimmeen/awesome-gnn4ts?

The main features of kimmeen/awesome-gnn4ts are: Awesome List, Research and Surveys, Research Surveys and Tutorials.

Which projects share features with kimmeen/awesome-gnn4ts?

Projects with overlapping indexed features include: qingsongedu/awesome-ssl4ts — A professionally curated list of awesome resources (paper, code, data, etc.) on Self-Supervised Learning for Time… xiaoxinhe/awesome-graph-llm — A collection of AWESOME things about Graph-Related Large Language Models (LLMs). qingsongedu/awesome-ai-for-time-series-papers — A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals. eugeneyan/open-llms — 📋 A list of open LLMs available for commercial use. mooler0410/llmspracticalguide — A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers). bradyfu/awesome-multimodal-large-language-models — :sparkles::sparkles:Latest Advances on Multimodal Large Language Models.