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community-detection

Community detection (i.e. graph clustering) project in Python.

https://github.com/mikolajlangner/community-detection

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (0.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Community detection (i.e. graph clustering) project in Python.

Basic Info
  • Host: GitHub
  • Owner: MikolajLangner
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 4.9 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme Changelog Contributing License Citation Authors

README.md

community-detection

Community detection (i.e. graph clustering) project in Python.

Owner

  • Name: Mikołaj Langner
  • Login: MikolajLangner
  • Kind: user

:trollface:

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Clustering via Random Walks
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Mikołaj
    name-particle: Mikołaj
    family-names: Langner
    email: mikolaj.langner@outlook.com
repository-code: 'https://github.com/MikolajLangner/community-detection'
abstract: >-
  Community detection is a fundamental task of complex
  networks analysis. The detection problem is noteworthy
  because there is no the best method --- various approaches
  guarantee different properties of detected communities.
  This paper describes the SynWalk method, introduces some
  extensions and compares it with classical algorithms based
  on random walks.
keywords:
  - community detection
  - random walks
  - louvain
  - graph clustering
version: 1.0.0

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Dependencies

requirements_dev.txt pypi
  • Sphinx ==1.8.5 development
  • black ==21.7b0 development
  • bump2version ==0.5.11 development
  • coverage ==4.5.4 development
  • flake8 ==3.7.8 development
  • pip ==19.2.3 development
  • pytest ==6.2.4 development
  • tox ==3.14.0 development
  • twine ==1.14.0 development
  • watchdog ==0.9.0 development
  • wheel ==0.33.6 development
requirements.txt pypi
  • setuptools *