Skip to main content

Graph algorithms

Project description

logo sknetwork https://img.shields.io/pypi/v/scikit-network.svg https://travis-ci.org/sknetwork-team/scikit-network.svg Documentation Status https://codecov.io/gh/sknetwork-team/scikit-network/branch/master/graph/badge.svg

Simple and efficient tools for the analysis of large graphs.

Quickstart

Install scikit-network:

$ pip install scikit-network

Import scikit-network in a Python project:

import sknetwork as skn

See examples in the tutorials; the notebooks are available here.

History

0.12.0 (2019-12-10)

  • Added VerboseMixin for verbosity features

  • Added Loaders for WikiLinks & Konect databases

0.11.0 (2019-11-28)

  • sknetwork: new API for bipartite graphs

  • new module: Soft node classification

  • new module: Node classification

  • new module: data (merge toy graphs + loader)

  • clustering: Spectral Clustering

  • ranking: new algorithms

  • utils: K-neighbors

  • hierarchy: Spectral Ward

  • data: loader (Vital Wikipedia)

0.10.1 (2019-08-26)

  • Minor bug

0.10.0 (2019-08-26)

  • Clustering (and related metrics) for directed and bipartite graphs

  • Hierarchical clustering (and related metrics) for directed and bipartite graphs

  • Fix bugs on embedding algorithms

0.9.0 (2019-07-24)

  • Change parser output

  • Fix bugs in ranking algorithms (zero-degree nodes)

  • Add notebooks

  • Import algorithms from scipy (shortest path, connected components, bfs/dfs)

  • Change SVD embedding (now in decreasing order of singular values)

0.8.2 (2019-07-19)

  • Minor bug

0.8.1 (2019-07-18)

  • Added diffusion ranking

  • Minor fixes

  • Minor doc tweaking

0.8.0 (2019-07-17)

  • Changed Louvain, BiLouvain, Paris and PageRank APIs

  • Changed PageRank method

  • Documentation overhaul

  • Improved Jupyter tutorials

0.7.1 (2019-07-04)

  • Added Algorithm class for nicer repr of some classes

  • Added Jupyter notebooks as tutorials in the docs

  • Minor fixes

0.7.0 (2019-06-24)

  • Updated PageRank

  • Added tests for Numba versioning

0.6.1 (2019-06-19)

  • Minor bug

0.6.0 (2019-06-19)

  • Largest connected component

  • Simplex projection

  • Sparse Low Rank Decomposition

  • Numba support for Paris

  • Various fixes and updates

0.5.0 (2019-04-18)

  • Unified Louvain.

0.4.0 (2019-04-03)

  • Added Louvain for directed graphs and ComboLouvain for bipartite graphs.

0.3.0 (2019-03-29)

  • Updated clustering module and documentation.

0.2.0 (2019-03-21)

  • First real release on PyPI.

0.1.1 (2018-05-29)

  • First release on PyPI.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit-network-0.12.0.tar.gz (77.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

scikit_network-0.12.0-py2.py3-none-any.whl (114.6 kB view details)

Uploaded Python 2Python 3

File details

Details for the file scikit-network-0.12.0.tar.gz.

File metadata

  • Download URL: scikit-network-0.12.0.tar.gz
  • Upload date:
  • Size: 77.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.40.2 CPython/3.6.7

File hashes

Hashes for scikit-network-0.12.0.tar.gz
Algorithm Hash digest
SHA256 962bef5b8116e58dee6af02be9c4274ff1cae66870173c115a20205269d8e85d
MD5 427f315305c3afb6990422809ba96699
BLAKE2b-256 17092f1bfbd4906b43b252d9247afe89a0c97d0bd87d2610224de64d7365ba4a

See more details on using hashes here.

File details

Details for the file scikit_network-0.12.0-py2.py3-none-any.whl.

File metadata

  • Download URL: scikit_network-0.12.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 114.6 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.40.2 CPython/3.6.7

File hashes

Hashes for scikit_network-0.12.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 f2b339d2335fdb9fdf0f7aabab6451be1025550092dcd38fc425a87a77f7b57f
MD5 0f2289b5ac72857bd0d71fa0319c3aeb
BLAKE2b-256 d6e4ab62d827426dbee10a1c9ea180d88c66056af9dacaf147634d9d12414cf0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page