https://github.com/gravins/anti-symmetricdgn

Official code repository for the papers "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" accepted at ICLR 2023; "Non-Dissipative Propagation by Anti-Symmetric Deep Graph Networks"; and "Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks"
https://github.com/gravins/anti-symmetricdgn

deep-graph-networks deep-learning graph-neural-networks graphneuralnetwork ordinary-differential-equations

Last synced: about 2 months ago
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Official code repository for the papers "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" accepted at ICLR 2023; "Non-Dissipative Propagation by Anti-Symmetric Deep Graph Networks"; and "Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks"

• Host: GitHub
• URL: https://github.com/gravins/anti-symmetricdgn
• Owner: gravins
• Created: 2023-01-27T18:28:34.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-26T15:10:32.000Z (6 months ago)
• Last Synced: 2024-06-26T18:37:06.269Z (6 months ago)
• Topics: deep-graph-networks, deep-learning, graph-neural-networks, graphneuralnetwork, ordinary-differential-equations
• Language: Python
• Homepage:
• Size: 8.98 MB
• Stars: 10
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Anti-Symmetric DGN

This repository provides the official reference implementation of our papers 

- **[Anti-Symmetric DGN: a stable architecture for Deep Graph Networks](https://openreview.net/forum?id=J3Y7cgZOOS)** accepted at ICLR 2023

- **[Non-Dissipative Propagation by Anti-Symmetric Deep Graph Networks](https://drive.google.com/file/d/1uPHhjwSa3g_hRvHwx6UnbMLgGN_cAqMu/view?usp=share_link)** accepted at the DLG-AAAI’23 workshop.

- **[Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks](http://pages.di.unipi.it/gravina/#publications)** accepted at the Deep Learning meets Neuromorphic Hardware Workshop, ECML-PKDD 2023.

          

Please consider citing us

	@inproceedings{gravina2023adgn,

		author = {Alessio Gravina and Davide Bacciu and Claudio Gallicchio},

	 	title = {{Anti-Symmetric DGN: a stable architecture for Deep Graph Networks}},

	 	booktitle = {The Eleventh International Conference on Learning Representations },

	 	year = {2023},

		url = {https://openreview.net/forum?id=J3Y7cgZOOS}

	}

 	@inproceedings{gravina2023randomized,

		author = {Alessio Gravina and Claudio Gallicchio and Davide Bacciu},

	 	title = {{Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks}},

	 	booktitle = {},

	 	year = {2023},

		url = {}

	}

## Requirements

_Note: we assume Miniconda/Anaconda is installed, otherwise see this [link](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html) for correct installation. The proper Python version is installed during the first step of the following procedure._

1. Install the required packages and create the environment

    - ``` conda env create -f env.yml ```

2. Activate the environment

    - ``` conda activate adgn ```

## Run the experiment

_Note: To run the experiment is fundamental to define the model name and dataset name into the run_all.sh files. For more details launch ```python3 main.py --help``` or ```python3 run-adgn-2-8.py --help```_

- Graph property prediction

    ```graph_prop_pred/run_all.sh```

- Graph benchmarks

    ```graph_benchmark/run_all.sh```

    - _Note: please ensure that conf_seeds_5.json and conf_seeds_5.json match the generated seed before running the experiment_

- Graph heterophilic benchmarks

    ```graph_heteropily/run_all.sh```

- Tree-NeighborsMatch

    ```Tree-NeighborsMatch/run_all.sh```

## Repository structure

The repository is structured as follows:

    ├── README.md                <- The top-level README.

    │

    ├── env.yml                  <- The conda environment requirements.

    │

    ├── conda_list_output.txt    <- The specific version of each package in the adgn environment.

    │

    ├── graph_benchmark          <- Contains the code to reproduce the graph benchmarks experiment.

    │    ├── conf_seeds_5.json   <- The configuration seeds used in the experiment.

    │    ├── data_seeds_5.json   <- The seeds used in the experiment to split the dataset into train/valid/test.

    │    ├── run_all.sh          <- The script used to run the experiment. Note: you need to specify the name of the model and the dataset (see conf.py and utils/__init__.py)

    │    ├── models              <- Contains the code for the framework A-DGN and other DGNs 

    │    ├── utils               <- Contains the code for data and io utilities.

    │    ├── main.py             <- The main.

    │    ├── train.py            <- Implements the code responsible for training and evaluation of the models.

    │    ├── conf.py             <- Contains the hyper-parameter space for each model.

    │    └── model_selction.py   <- Implements the model selection and produces the report of the results.

    │

    └── graph_prop_pred          <- Contains the code to reproduce the graph property prediction experiment.

    │   ├── run_all.sh           <- The script used to run the experiment. Note: you need to specify the name of the model and the dataset (see conf.py and utils/__init__.py)

    │   ├── models               <- Contains the code for the framework A-DGN and other DGNs 

    │   ├── utils                <- Contains the code for data and io utilities.

    │   ├── main.py              <- The main.

    │   ├── train_GraphProp.py   <- Implements the code responsible for training and evaluation of the models.

    │   ├── conf.py              <- Contains the hyper-parameter space for each model.

    │   └── model_selction.py    <- Implements the model selection and produces the report of the results.

    │    

    ├── graph_heteropily          <- Contains the code to reproduce the graph heterophilic benchmarks experiment.

    │    ├── run_all.sh          <- The script used to run the experiment. Note: you need to specify the name of the model and the dataset (see conf.py and utils/__init__.py)

    │    ├── models              <- Contains the code for the framework A-DGN and other DGNs 

    │    ├── utils               <- Contains the code for data and io utilities.

    │    ├── main.py             <- The main.

    │    ├── train.py            <- Implements the code responsible for training and evaluation of the models.

    │    ├── conf.py             <- Contains the hyper-parameter space for each model.

    │    └── model_selction.py   <- Implements the model selection and produces the report of the results.

    │

    └── Tree-NeighborsMatch      <- Contains the code to reproduce the Tree-NeighborsMatch experiment retrieved from the original source code: https://github.com/tech-srl/bottleneck.

        ├── run_all.sh           <- The script used to run the experiment. Note: you need to specify the name of the model and the dataset (see conf.py and utils/__init__.py)

        └── run-adgn-2-8.py      <- The main.