# yueliu1999/Awesome-Deep-Graph-Clustering

[IEEE T-KDE 2026] Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

Repository: https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering
Canonical: https://ross.abutalabs.com/products/awesome-deep-graph-clustering
Language: Python
License: MIT
License Family: permissive
Topics: deep-clustering, graph-neural-networks, self-supervised-learning, representation-learning, surveys, data-mining, deep-learning, graph-convolutional-networks, graph-embedding, network-embedding, gcn, machine-learning, clustering, data-mining-algorithms, graphclustering
Last push: 2026-06-07T01:52:45+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": 1743, "days_push": 88, "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 1016, forks 155 (observed 2026-08-28T04:03:14.491208+00:00)

## What it is
A curated awesome-list of state-of-the-art deep graph clustering methods, including papers, code links, and datasets. It accompanies an IEEE T-KDE survey on deep graph clustering.

## Use cases
- find papers on deep graph clustering
- survey of graph neural network clustering methods
- datasets for node clustering benchmarks
- keep up with SOTA graph clustering research
- find code implementations of graph clustering papers
- literature review for graph representation learning

## When to choose
- you need a curated, regularly updated index of deep graph clustering papers and code
- you are doing a literature survey or literature review on graph clustering
- you want benchmark datasets and reference implementations in one place

## When to avoid
- you need a ready-to-use clustering library rather than a paper collection
- you need general-purpose graph learning tools beyond clustering
- you expect production software with support or guarantees

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, data-science
- platform: python
- tags: awesome-list, graph-clustering, graph-neural-networks, papers, survey, self-supervised-learning, representation-learning, algorithms

## Member repositories
- yueliu1999/Awesome-Deep-Graph-Clustering (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:14.491208+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 2026-08-30T07:11:22.499948+00:00, confidence not recorded.
  - readme: https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering (fetched 2026-08-28T04:03:14.491208+00:00, sha 595c6c172a21)
- Data as of 2026-08-30T08:39:29.467469+00:00.