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

0.11.0 (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-0.11.0.tar.gz (76.0 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-0.11.0-py2.py3-none-any.whl (112.2 kB view details)

Uploaded Python 2Python 3

File details

Details for the file scikit-network-0.11.0.tar.gz.

File metadata

  • Download URL: scikit-network-0.11.0.tar.gz
  • Upload date:
  • Size: 76.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.39.0 CPython/3.6.7

File hashes

Hashes for scikit-network-0.11.0.tar.gz
Algorithm Hash digest
SHA256 9235e62bb547ee9a0e8c7ed867e8dfb46f3c5fee52b06fe379bd564beb8bd90e
MD5 d70ec9f50b2764e4c02b0cc076b86da7
BLAKE2b-256 2871759afe2aa6b293cbd924224544b4837a50b295222d9e0affb6aaeb184db2

See more details on using hashes here.

File details

Details for the file scikit_network-0.11.0-py2.py3-none-any.whl.

File metadata

  • Download URL: scikit_network-0.11.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 112.2 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/42.0.1 requests-toolbelt/0.9.1 tqdm/4.39.0 CPython/3.6.7

File hashes

Hashes for scikit_network-0.11.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 a53a4974ef7d187ac555679ed8510778d19f29dd2ce2c0b1909d222990d57762
MD5 aed723bf0765ca795a94bfe8ae2cb9a8
BLAKE2b-256 752d19a9178fef7b5173847944c9aaeb1ac3b74ce60fb5ebb8bfcd8c6a4f4e4e

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