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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

Python package inspired by scikit-learn 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

0.16.0 (2020-04-30)

  • Refactor basics module into connectivity

  • Cython version for label propagation

  • Minor corrections

0.15.2 (2020-04-24)

  • Clarified requirements

  • Minor corrections

0.15.1 (2020-04-21)

  • Added OpenMP support for all platforms

0.15.0 (2020-04-20)

  • Updated ranking module : new pagerank solver, new HITS params, post-processing

  • Polynomes in linear algebra

  • Added meta.name attribute for Bunch

  • Minor corrections

0.14.0 (2020-04-17)

  • Added spring layout in embedding

  • Added label propagation in classification

  • Added save / load functions in data

  • Added display edges parameter in svg graph exports

  • Corrected typos in documentation

0.13.3 (2020-04-13)

  • Minor bug

0.13.2 (2020-04-13)

  • Added wheels for multiple platforms (OSX, Windows (32 & 64 bits) and many Linux) and Python version (3.6/3.7/3.8)

  • Documentation update (SVG dendrograms, tutorial updates)

0.13.1a (2020-04-09)

  • Minor bug

0.13.0a (2020-04-09)

  • Changed from Numba to Cython for better performance

  • Added visualization module

  • Added k-nearest neighbors classifier

  • Added Louvain hierarchy

  • Added predict method in embedding

  • Added soft clustering to clustering algorithms

  • Added soft classification to classification algorithms

  • Added graphs in data module

  • Various API change

0.12.1 (2020-01-20)

  • Added heat kernel based node classifier

  • Updated loaders for WikiLinks

  • Fixed file-related issues for Windows

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 WardDense

  • 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.

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