# naganandy/graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

Repository: https://github.com/naganandy/graph-based-deep-learning-literature
Canonical: https://ross.abutalabs.com/products/graph-based-deep-learning-literature
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: graph-neural-networks, graph-convolutional-networks, graph, deep-learning, neural-networks, graph-representation-learning, conference-publications
Last push: 2026-06-07T20:24:09+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 86, release rhythm 35, longevity 100
- inputs: {"age_days": 3197, "days_push": 87, "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 5096, forks 782 (observed 2026-08-28T04:09:09.692807+00:00)

## What it is
A curated collection of links to conference publications on graph-based deep learning, organized by conference, year, and topic. It also indexes related workshops, surveys, literature reviews, books, and software libraries.

## Use cases
- find papers on graph neural networks from NeurIPS or ICML
- catch up on recent graph deep learning research by year
- find surveys and literature reviews on graph representation learning
- discover workshops related to graph neural networks
- locate software libraries for graph deep learning
- build a reading list for graph convolutional networks

## When to choose
- you need a curated, topic-organized index of GNN research papers
- you want to track graph deep learning publications across major ML conferences over multiple years
- you are starting research in graph representation learning and need an entry point

## When to avoid
- you need runnable code or a software library rather than paper links
- you need full-text papers rather than links to publication pages
- you need non-graph deep learning literature

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, documentation
- domain: deep-learning, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: graph-neural-networks, graph-convolutional-networks, literature-review, conference-publications, curated-list, graph-representation-learning

## Member repositories
- naganandy/graph-based-deep-learning-literature (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.692807+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 2026-08-29T18:02:14.337770+00:00, confidence not recorded.
  - readme: https://github.com/naganandy/graph-based-deep-learning-literature (fetched 2026-08-28T04:09:09.692807+00:00, sha a8d06545ed99)
- Data as of 2026-08-30T08:39:29.467469+00:00.