Refactor karateclub dependency

README.md

@@ -13,9 +13,8 @@
   <a href="#-references">References</a>
 </p>
 
-
 <div align="center">
-   
+
 [![Test](https://github.com/pyt-team/TopoEmbedX/actions/workflows/test.yml/badge.svg)](https://github.com/pyt-team/TopoEmbedX/actions/workflows/test.yml)
 [![Lint](https://github.com/pyt-team/TopoEmbedX/actions/workflows/lint.yml/badge.svg)](https://github.com/pyt-team/TopoEmbedX/actions/workflows/lint.yml)
 [![Codecov](https://codecov.io/gh/pyt-team/TopoEmbedX/branch/main/graph/badge.svg)](https://app.codecov.io/gh/pyt-team/TopoEmbedX)
@@ -28,8 +27,6 @@
 
 </div>
 
-
-
 ![topoembedx](https://user-images.githubusercontent.com/8267869/234074436-402ac931-2dc9-43da-a056-6c927f613242.png)
 
 Many natural systems as diverse as social networks and proteins are characterized by _relational structure_. This is the structure of interactions between components in the system, such as social interactions between individuals or electrostatic interactions between atoms.
@@ -38,49 +35,28 @@ How can we conveniently represent data defined on such relational systems?
 
 `TopoEmbedX` (TEX) is a package for representation learning on topological domains, the mathematical structures of relational systems.
 
-
 ## 🛠️ Main Features
 
 Support of higher order representation learning algorithms such as:
+
 - DeepCell,
 - Cell2Vec,
 - Higher Order Laplacian Eigenmaps, and
 - Higher Order Geometric Laplacian Eigenmaps
 
 for the topological domains supported in [TopoNetX](https://github.com/pyt-team/TopoNetX).
 
-
 ## 🤖 Installing TopoEmbedX
 
+`TopoEmbedX` is available on PyPI and can be installed using `pip`.
 
-We recommend using Python 3.11, which is a python version used to run the unit-tests.
-
-For example, create a conda environment:
-   ```bash
-   conda create -n tex python=3.11.3
-   conda activate tex
-   ```
-
-Then:
-
-1. Clone a copy of `TopoEmbedX` from source:
-```bash
-git clone https://github.com/pyt-team/TopoEmbedX
-cd TopoEmbedX
-```
-2. If you have already cloned `TopoEmbedX` from source, update it:
-```bash
-git pull
-```
-3. Install `TopoEmbedX` in editable mode:
-```bash
-pip install -e '.[all]'
-```
-4. Install pre-commit hooks:
 ```bash
-pre-commit install
+pip install "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de"
+pip install topoembedx
 ```
 
+The library depends on `karateclub`, which did not receive updates on PyPI for a long time.
+You have to manually install the latest version from GitHub for up-to-date NetworkX compatibility.
 
 ## 🦾 Getting Started
 
@@ -107,11 +83,47 @@ model.fit(cc, neighborhood_type="adj", neighborhood_dim={"rank": 1, "via_rank":
 embeddings = model.get_embedding()
 ```
 
-## 🔍 References ##
+## 🧑‍💻 Install from source
+
+To install the latest version from source, follow these steps:
+
+1. Clone a copy of `TopoEmbedX` from source:
+
+```bash
+git clone https://github.com/pyt-team/TopoEmbedX
+cd TopoEmbedX
+```
+
+2. If you have already cloned `TopoEmbedX` from source, update it:
+
+```bash
+git pull
+```
+
+3. Install a recent version of `karateclub`:
+
+```bash
+pip install "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de"
+```
+
+4. Install `TopoEmbedX` in editable mode:
+
+```bash
+pip install -e '.[all]'
+```
+
+5. Install pre-commit hooks:
+
+```bash
+pre-commit install
+```
+
+## 🔍 References
 
 To learn more about topological representation learning.
 
 - Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub. [Topological Deep Learning: Going Beyond Graph Data](https://arxiv.org/abs/2206.00606).
+
 ```
 @misc{hajij2023topological,
       title={Topological Deep Learning: Going Beyond Graph Data},
@@ -124,7 +136,9 @@ To learn more about topological representation learning.
 ```
 
 Figure from:
+
 - Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane. [Architectures of Topological Deep Learning: A Survey on Topological Neural Networks.](https://arxiv.org/pdf/2304.10031.pdf)
+
 ```
 @misc{papillon2023architectures,
       title={Architectures of Topological Deep Learning: A Survey on Topological Neural Networks},

docs/index.rst

@@ -1,14 +1,14 @@
 🌐 TopoEmbedX (TEX) 🍩
 =======================
 
-`TopoEmbedX` (TEX) is a Python package for representation Learning on Topological Domains. Topological domains are the natural mathematical structures representing relations between the components of a dataset.
+``TopoEmbedX`` (TEX) is a Python package for representation Learning on Topological Domains. Topological domains are the natural mathematical structures representing relations between the components of a dataset.
 
 .. figure:: https://user-images.githubusercontent.com/8267869/234074436-402ac931-2dc9-43da-a056-6c927f613242.png
    :alt: topoembedx
    :class: with-shadow
    :width: 1000px
 
-Many natural systems as diverse as social networks and proteins are characterized by *relational structure*. This is the structure of interactions between components in the system, such as social interactions between individuals or electrostatic interactions between atoms. How can we conveniently represent data defined on such relational systems? `TopoEmbedX` (TEX) is a package for representation learning on topological domains, the mathematical structures of relational systems.
+Many natural systems as diverse as social networks and proteins are characterized by *relational structure*. This is the structure of interactions between components in the system, such as social interactions between individuals or electrostatic interactions between atoms. How can we conveniently represent data defined on such relational systems? ``TopoEmbedX`` (TEX) is a package for representation learning on topological domains, the mathematical structures of relational systems.
 
 
 🛠️ Main Features
@@ -23,6 +23,19 @@ Support of higher order representation learning algorithms such as:
 
 for the topological domains supported in `TopoNetX <https://github.com/pyt-team/TopoNetX>`__.
 
+🤖 Installing TopoEmbedX
+----------------------
+
+``TopoEmbedX`` is available on PyPI and can be installed using ``pip``:
+
+.. code-block:: bash
+
+   pip install "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de"
+   pip install topoembedx
+
+The library depends on ``karateclub``, which did not receive updates on PyPI for a long time.
+You have to manually install the latest version from GitHub for up-to-date NetworkX compatibility.
+
 🔍 References
 -------------
 

pyproject.toml

@@ -31,7 +31,9 @@ classifiers = [
 requires-python = ">= 3.10"
 dependencies=[
     "numpy",
-    "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de",
+    # karateclub did not receive updates on PyPI for a long time.
+    # We ask the user to manually install a recent version from GitHub.
+    "karateclub",
     "networkx",
     "pyrandwalk",
     "scipy",