Python package for the analysis of large graphs:
Memory-efficient representation as sparse matrices in the CSR format of scipy
Fast algorithms
Simple API inspired by scikit-learn
Resources
Free software: BSD license
Documentation: https://scikit-network.readthedocs.io
Quick Start
Install scikit-network:
$ pip install scikit-networkImport scikit-network in a Python project:
import sknetwork as skn
See examples in the tutorials; the notebooks are available here.
Citing
If you want to cite scikit-network, please refer to the publication in the Journal of Machine Learning Research:
@article{JMLR:v21:20-412,
author = {Thomas Bonald and Nathan de Lara and Quentin Lutz and Bertrand Charpentier},
title = {Scikit-network: Graph Analysis in Python},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {185},
pages = {1-6},
url = {http://jmlr.org/papers/v21/20-412.html}
}History
0.22.0 (2021-02-09)
Added hierarchical Louvain embedding by Quentin Lutz (#468)
Doc fixes and updates
Requirements update
0.21.0 (2021-01-29)
Added random projection embedding by Thomas Bonald (#461)
Added PCA-based embedding by Thomas Bonald (#461)
Added 64-bit support for Louvain by Flávio Juvenal (#450)
Added verbosity options for dataset loaders
Fixed Louvain embedding
Various doc and tutorial updates
0.20.0 (2020-10-20)
Added betweenness algorithm by Tiphaine Viard (#444)
0.19.3 (2020-09-17)
Added Louvain-based embedding
Fix documentation with new dataset website URLs
0.19.2 (2020-09-14)
Fix documentation with new dataset website URLs.
0.19.1 (2020-09-09)
Fix visualization features
Fix documentation
0.19.0 (2020-09-02)
Added link prediction module
Added pie-node visualization of memberships
Added Weisfeiler-Lehman graph coloring by Pierre Pebereau and Alexis Barreaux (#394)
Added Force Atlas 2 graph layout by Victor Manach and Rémi Jaylet (#396)
Added triangle listing algorithm for directed and undirected graph by Julien Simonnet and Yohann Robert (#376)
Added k-core decomposition algorithm by Julien Simonnet and Yohann Robert (#377)
Added k-clique listing algorithm by Julien Simonnet and Yohann Robert (#377)
Added color map option in visualization module
Updated NetSet URL
0.18.0 (2020-06-08)
Added Katz centrality
Refactor connectivity module into paths and topology
Refactor Diffusion into Dirichlet
Added parsers for adjacency list TSV and GraphML
Added shortest paths and distances
0.17.0 (2020-05-07)
Add clustering by label propagation
Add models
Add function to build graph from edge list
Change a parameter in SVG visualization functions
Minor corrections
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.
Release files for scikit-network 0.22.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
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|---|---|---|---|---|
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| scikit_network-0.22.0-cp38-cp38-win32.whl | CPython 3.8 | CPython 3.8 | Windows x86-32 | Details |
| scikit_network-0.22.0-cp38-cp38-macosx_10_9_x86_64.whl | CPython 3.8 | CPython 3.8 | macOS 10.9+ x86-64 | Details |
| scikit_network-0.22.0-cp37-cp37m-win_amd64.whl | CPython 3.7 | CPython 3.7 pymalloc | Windows x86-64 | Details |
| scikit_network-0.22.0-cp37-cp37m-win32.whl | CPython 3.7 | CPython 3.7 pymalloc | Windows x86-32 | Details |
| scikit_network-0.22.0-cp37-cp37m-macosx_10_9_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | macOS 10.9+ x86-64 | Details |
| scikit_network-0.22.0-cp36-cp36m-win_amd64.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-64 | Details |
| scikit_network-0.22.0-cp36-cp36m-win32.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-32 | Details |
| scikit_network-0.22.0-cp36-cp36m-macosx_10_9_x86_64.whl | CPython 3.6 | CPython 3.6 pymalloc | macOS 10.9+ x86-64 | Details |
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