# DeepGraphLearning/LiteratureDL4Graph

A comprehensive collection of recent papers on graph deep learning

Repository: https://github.com/DeepGraphLearning/LiteratureDL4Graph
Canonical: https://ross.abutalabs.com/products/literaturedl4graph
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, papers, arxiv
Last push: 2020-12-20T23:21:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2631, "days_push": 2082, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3103, forks 559 (observed 2026-08-28T04:07:43.707500+00:00)

## What it is
A curated, organized list of research papers on deep learning for graphs, covering topics like node representation learning, graph embeddings, and graph neural networks. Papers are indexed by topic and venue with authors and keywords.

## Use cases
- find papers on graph neural networks
- learn about node embedding methods like DeepWalk and node2vec
- survey the literature on deep learning for graphs
- find GNN papers by venue or topic
- get started researching graph representation learning

## When to choose
- you need a curated reading list for graph deep learning research
- you want papers organized by topic and venue
- you are surveying graph embedding and GNN literature

## When to avoid
- you need runnable code or a software library
- you need up-to-date papers after 2020
- you want tutorials rather than paper references

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: machine-learning, deep-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: graph-neural-networks, paper-list, graph-embedding, arxiv, reading-list

## Member repositories
- DeepGraphLearning/LiteratureDL4Graph (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:43.707500+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:46:11.667710+00:00, confidence not recorded.
  - readme: https://github.com/DeepGraphLearning/LiteratureDL4Graph (fetched 2026-08-28T04:07:43.707500+00:00, sha 0ba561fe105e)
- Data as of 2026-08-30T08:39:29.467469+00:00.