Merge branch 'develop'

Author: tbonald

.github/workflows/wheels_build.yml

@@ -20,11 +20,10 @@ jobs:
         env:
           CIBW_PLATFORM: auto
           CIBW_ARCHS: auto64
-          CIBW_SKIP: cp*-musllinux*
+          CIBW_SKIP: cp*-musllinux* *-win32
           CIBW_ARCHS_MACOS: x86_64 arm64
           CIBW_BUILD_VERBOSITY: 3
           CIBW_BEFORE_BUILD: "pip install -r requirements_dev.txt && pip install ."
-          CIBW_BEFORE_BUILD_MACOS: "pip install -r requirements_dev.txt && pip install ."
           CIBW_BUILD: cp39-* cp310-* cp311-* cp312-* cp313-*
 
       - uses: actions/upload-artifact@v4

HISTORY.rst

@@ -2,6 +2,12 @@
 History
 =======
 
+0.33.4 (2025-11-14)
+-------------------
+
+* Fix Windows wheel build #589
+* Fix MacOS wheel build #591
+
 0.33.3 (2025-05-15)
 -------------------
 

docs/first_steps.rst

@@ -42,11 +42,11 @@ Import scikit-network in Python:
 
     import sknetwork as skn
 
-Usage
------
+Get started
+-----------
 
-A graph is represented by its :term:`adjacency` matrix (square matrix). When the graph is bipartite, it can be represented by its :term:`biadjacency` matrix (rectangular matrix).
-Check our :ref:`tutorial<DataTag>` for various ways of loading a graph
+A graph is represented by its :term:`adjacency` matrix. When the graph is bipartite,
+it can be represented by its :term:`biadjacency` matrix (most often, a rectangular matrix). Check our :ref:`tutorial<DataTag>` for various ways of loading a graph
 (from a list of edges, a dataframe or a CSV file, for instance).
 
 Each algorithm is represented as an object with a ``fit`` method.
@@ -63,6 +63,10 @@ Here is an example to cluster the `Karate club graph`_ with the `Louvain algorit
     algorithm.fit(adjacency)
 
 
+If the graph is bipartite, the algorithm applies to
+nodes corresponding to the rows of the :term:`biadjacency` matrix; specific outputs for nodes corresponding to
+rows and columns of the biadjacency matrix can be obtained with the respective suffixes `_row_` and `_col_`.
+
 More details are provided in this :ref:`tutorial<OverviewTag>`.
 
 .. _Karate club graph: https://en.wikipedia.org/wiki/Zachary%27s_karate_club

docs/index.rst

@@ -5,7 +5,7 @@ Welcome to scikit-network's documentation!
 
 .. toctree::
    :maxdepth: 1
-   :caption: Getting started
+   :caption: Introduction
 
    first_steps
 
@@ -48,7 +48,7 @@ Welcome to scikit-network's documentation!
 
 .. toctree::
    :maxdepth: 1
-   :caption: Examples
+   :caption: Use cases
 
    use_cases/text
    use_cases/wikipedia

docs/tutorials/data/load_collection.ipynb

@@ -2,14 +2,22 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# Datasets"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "This tutorial shows how to load graphs from the [NetSet](https://netset.telecom-paris.fr) collection."
    ]
@@ -23,7 +31,8 @@
      "start_time": "2019-11-25T09:09:39.018204Z"
     },
     "pycharm": {
-     "is_executing": false
+     "is_executing": false,
+     "name": "#%%\n"
     }
    },
    "outputs": [],
@@ -33,7 +42,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## NetSet\n",
     "\n",
@@ -43,7 +56,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "dataset = load_netset('openflights')\n",
@@ -54,7 +71,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# to get all fields\n",
@@ -64,7 +85,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Directed graph\n",
@@ -77,7 +102,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# Bipartite graph\n",
@@ -129,4 +158,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}