# benedekrozemberczki/awesome-community-detection

A curated list of community detection research papers with implementations.

Repository: https://github.com/benedekrozemberczki/awesome-community-detection
Canonical: https://ross.abutalabs.com/products/awesome-community-detection
Language: Python
License: CC0-1.0
License Family: permissive
Topics: graph-clustering, network-clustering, clustering, dimensionality-reduction, factorization, embedding, deep-learning, matrix-factorization, igraph, networkx, bigclam, network-science, social-network, network-analysis, node2vec, deepwalk, unsupervised-learning, data-science, machine-learning, community-detection
Last push: 2025-12-20T17:06:00+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 58, release rhythm 8, longevity 100
- inputs: {"age_days": 2884, "days_push": 256, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2450, forks 357 (observed 2026-08-28T04:06:52.905001+00:00)

## What it is
A curated awesome-list of community detection research papers, organized by method family (matrix factorization, deep learning, spectral, temporal, hypergraphs, etc.), with links to implementations. It also points to relevant libraries and related paper collections.

## Use cases
- find community detection algorithms for a social network analysis project
- research papers on graph clustering with code
- compare Louvain, BigCLAM, and deep learning community detection methods
- find implementations of node2vec and DeepWalk style graph embeddings
- get started learning network science and graph partitioning
- find libraries for community detection in Python

## When to choose
- you want a survey-style index of community detection literature with runnable implementations
- you are doing academic research or literature review on graph clustering
- you need to discover libraries like igraph or networkx-based tools for network analysis

## When to avoid
- you need a ready-to-use community detection library rather than a paper index
- you need production graph analytics infrastructure, not references
- your problem is not graph/network related

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, developer-tools
- domain: machine-learning, data-science, social-media, tutorials
- platform: python
- tags: awesome-list, community-detection, graph-clustering, network-analysis, research-papers, graph-embedding, algorithms

## Member repositories
- benedekrozemberczki/awesome-community-detection (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:52.905001+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 2026-08-30T02:30:13.520165+00:00, confidence not recorded.
  - readme: https://github.com/benedekrozemberczki/awesome-community-detection (fetched 2026-08-28T04:06:52.905001+00:00, sha 3086ed9c1522)
- Data as of 2026-08-30T08:39:29.467469+00:00.