thunlp/gnnpapers

★ 16,766Audience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((GNNPapers))
    Surveys
      Overview papers
      Field evolution
    Models
      Basic architectures
      Pooling methods
      Explainability
      Efficiency
    Applications
      Knowledge graphs
      Chemistry biology
      NLP and vision
      Social networks
    Audience
      Researchers
      Students
      Literature review

mindmap root((GNNPapers)) Surveys Overview papers Field evolution Models Basic architectures Pooling methods Explainability Efficiency Applications Knowledge graphs Chemistry biology NLP and vision Social networks Audience Researchers Students Literature review

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Things people build with this

USE CASE 1

Use as a structured entry point when starting research or self-study on graph neural networks

USE CASE 2

Build a literature review bibliography by browsing papers organized by GNN architecture type or application domain

USE CASE 3

Track how the GNN field evolved from basic architectures through pooling, explainability, and efficiency research

Tech stack

Markdown

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

GNNPapers is a curated reading list of academic papers on graph neural networks, abbreviated as GNN. A graph neural network is a type of machine learning model designed to work on data that is naturally shaped like a network, nodes connected by edges, rather than on plain images, text, or tables. Examples include social networks, knowledge graphs, molecules, and road maps. The repository itself is not a piece of software. It is a single long README that lists must-read papers under thematic sections, with each entry showing the paper title, the conference or journal it appeared in, the year, a link to the paper, and the authors. The contents are organised into three main parts. The first is surveys, which give broad overviews of the field. The second is models, broken down into basic architectures, graph types, pooling methods, analysis, efficiency, and explainability. The third is applications, where graph neural networks have been tried out, including physics, chemistry and biology, knowledge graphs, recommender systems, computer vision, natural language processing, generation, combinatorial optimisation, adversarial attack, graph clustering, reinforcement learning, traffic networks, few-shot learning, program representation, and social networks. You would visit this repository when you are starting research or self-study in graph neural networks and want a structured entry point, when you want to follow how the field has evolved, or when you are writing a literature review and need a starting bibliography. The list was contributed by researchers Jie Zhou, Ganqu Cui, Zhengyan Zhang, and Yushi Bai. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

Using the GNNPapers list as a reference, recommend which five foundational papers I should read first to understand message-passing neural networks and graph convolutions.

Prompt 2

I'm applying graph neural networks to molecule property prediction. Which papers in the chemistry and biology applications section of GNNPapers should I prioritize, and what are their key contributions?

Prompt 3

Using GNNPapers as a starting bibliography, help me outline a literature review on GNN explainability, covering the key papers from that section and their main findings.

Prompt 4

Which papers in GNNPapers cover graph neural networks for recommender systems? List the main approaches and identify the most-cited ones I should read first.

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