scikit-network 0.21.0

Graph algorithms

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

• GitHub: https://github.com/sknetwork-team/scikit-network

• Documentation: https://scikit-network.readthedocs.io

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.

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

0.21.0 (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.