diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index c3c6100..619adc8 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -31,7 +31,7 @@ jobs:
cache-dependency-path: "pyproject.toml"
- name: Install dependencies [pip]
run: |
- pip install --upgrade pip setuptools wheel
+ pip install "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de"
pip install -e .[doc]
- name: Install Pandoc [apt-get]
run: |
@@ -67,6 +67,7 @@ jobs:
cache-dependency-path: "pyproject.toml"
- name: Install dependencies [pip]
run: |
+ pip install "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de"
pip install -e .[doc]
- name: Checking NumpyDoc Validation for files
run: |
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 527b443..14f8898 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -41,7 +41,7 @@ jobs:
- name: Install main package [pip]
run: |
- pip install --upgrade pip setuptools wheel
+ pip install "karateclub @ git+https://github.com/benedekrozemberczki/karateclub@cb46a91df8dcbeb2570debcf6a9d0c518107a2de"
pip install -e .[all]
- name: Typecheck [mypy]
run: |
diff --git a/README.md b/README.md
index ff3b14e..3214e30 100644
--- a/README.md
+++ b/README.md
@@ -13,9 +13,8 @@
References
-
-
+
[](https://github.com/pyt-team/TopoEmbedX/actions/workflows/test.yml)
[](https://github.com/pyt-team/TopoEmbedX/actions/workflows/lint.yml)
[](https://app.codecov.io/gh/pyt-team/TopoEmbedX)
@@ -28,8 +27,6 @@
-
-
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.
@@ -38,10 +35,10 @@ 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.
-
## π οΈ Main Features
Support of higher order representation learning algorithms such as:
+
- DeepCell,
- Cell2Vec,
- Higher Order Laplacian Eigenmaps, and
@@ -49,38 +46,17 @@ Support of higher order representation learning algorithms such as:
for the topological domains supported in [TopoNetX](https://github.com/pyt-team/TopoNetX).
-
## π€ 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.
## π¦Ύ Getting Started
@@ -107,11 +83,47 @@ model.fit(cc, neighborhood_type="adj", neighborhood_dim={"rank": 1, "via_rank":
embeddings = model.get_embedding()
```
-## π References ##
+## π§βπ» 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
+```
+
+## π References
To learn more about topological representation learning.
- Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo GuzmΓ‘n-SΓ‘enz, 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},
diff --git a/docs/index.rst b/docs/index.rst
index 8247e05..27e2a36 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,14 +1,14 @@
π TopoEmbedX (TEX) π©
=======================
-`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.
π οΈ Main Features
@@ -23,6 +23,19 @@ Support of higher order representation learning algorithms such as:
for the topological domains supported in `TopoNetX `__.
+π€ 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.
+
π References
-------------
diff --git a/pyproject.toml b/pyproject.toml
index af93bfa..ac5a2e7 100644
--- a/pyproject.toml
+++ b/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",