Graph algorithms
Project description
Simple and efficient tools for the analysis of large graphs.
Free software: BSD license
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.
History
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
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