ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Official repository for the Topological Deep Learning Challenge 2024, jointly organized by TAG-DS & PyT-Team and hosted by the Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop at ICML 2024.

Relevant Information

The deadline is July 12th, 2024 (Anywhere on Earth). Participants are welcome to modify their submissions until this time.
Please, check out the main webpage of the challenge for the full description of the competition (motivation, submission requirements, evaluation, etc.)

Brief Description

The main purpose of the challenge is to further expand the current scope and impact of Topological Deep Learning (TDL), enabling the exploration of its applicability in new contexts and scenarios. To do so, we propose participants to design and implement lifting mappings between different data structures and topological domains (point-clouds, graphs, hypergraphs, simplicial/cell/combinatorial complexes), potentially bridging the gap between TDL and all kinds of existing datasets.

General Guidelines

Everyone can participate and participation is free --only principal PyT-Team developers are excluded. It is sufficient to:

Send a valid Pull Request (i.e. passing all tests) before the deadline.
Respect Submission Requirements (see below).

Teams are accepted, and there is no restriction on the number of team members. An acceptable Pull Request automatically subscribes a participant/team to the challenge.

We encourage participants to start submitting their Pull Request early on, as this helps addressing potential issues with the code. Moreover, earlier Pull Requests will be given priority consideration in the case of multiple submissions of similar quality implementing the same lifting.

A Pull Request should contain no more than one lifting. However, there is no restriction on the number of submissions (Pull Requests) per participant/team.

Basic Setup

To develop on your machine, here are some tips.

First, we recommend using Python 3.11.3, which is the python version used to run the unit-tests.

For example, create a conda environment:

conda create -n topox python=3.11.3
conda activate topox

Then:

Clone a copy of tmx from source:

git clone git@github.com:pyt-team/challenge-icml-2024.git
cd challenge-icml-2024

Install tmx in editable mode:
```
pip install -e '.[all]'
```
Notes:
- Requires pip >= 21.3. Refer: PEP 660.
- On Windows, use pip install -e .[all] instead (without quotes around [all]).

Install torch, torch-scatter, torch-sparse with or without CUDA depending on your needs.

pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/${CUDA}
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+${CUDA}.html
pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html

where ${CUDA} should be replaced by either cpu, cu102, cu113, or cu115 depending on your PyTorch installation (torch.version.cuda).

Ensure that you have a working tmx installation by running the entire test suite with
```
pytest
```
In case an error occurs, please first check if all sub-packages (torch-scatter, torch-sparse, torch-cluster and torch-spline-conv) are on its latest reported version.
Install pre-commit hooks:
```
pre-commit install
```

Questions