SHGLNN

Self-supervised Heterogeneous Hypergraph Learning with Context-aware Pooling for Graph-level Classification

SHGLNN is an algorithm for graph embeddings, harnessing the power of hypergraphs for intra and inter-graph contexts. This repository provides the code implementation of the algorithm as described in the paper.

Algorithm Overview

The algorithm receives a set of graphs and processes them in batches through a specified number of epochs. It leverages intra-graph hyperedges, inter-graph hyperedges, type-specific node and hyperedge attentions, and contrastive loss to generate embeddings for the input graphs.

See Algorithm 1 in our paper for a detailed step-by-step explanation.

Code Structure

Core Files:

• kMatrix.py: Responsible for generating k-specific matrices corresponding to intra-graph hyperedges.
• sMatrix.py: Generates matrix pertaining to inter-graph hyperedges.
• hypergraphGen.py: Constructs hypergraph using the intra and inter-graph hyperedges.
• dataset_loader.py: Helper functions to load and preprocess the datasets.
• layers.py: Contains various neural network layers utilized in the model - node convolution, hyperedge convolution, context-aware graph-level pooling
• model.py: Defines the main SHGLNN neural network model.
• train.py: Orchestrates the training process of the algorithm, including the calculation of contrastive loss and back propagation.

Prerequisites

• Python 3.8 or above
• PyTorch Geometric 2.4.0
• NumPy 1.26.0
• NetworkX 3.1
• HypernetX 2.0.5

Datasets

We use the TUDatasets mentioned in the paper from PyTorch Geometric TUDatasets (https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.datasets.TUDataset.html#torch_geometric.datasets.TUDataset)

Usage

1. Clone the repository git clone https://github.com/YOUR_USERNAME/SHGLNN.git cd SHGLNN
2. Install the required packages/libraries: pip install numpy networkx hypernetx
3. Generate the matrices K and S for intra- and inter-graph hyperedges
   • Note that for all datasets, multiple K matrices would be generated as we go through different values of K. However, we will generate a single S matrix for each dataset.
4. Generate hypergraph using hypergraphGen.py
5. Develop layers using layers.py
6. Run the model.py to develop the model
7. Then, train the model using train.py
   • Note that for datasets, we used the built-in TU dataset from PyTorch Geometric

Contribution

Pull requests are welcome. For major changes or any issues, please open an issue first for discussion. For further inquiries, contact the first author at malik.khizar@hdr.mq.edu.au or khizerhayat92@gmail.com.

License

MIT

Citation

If you use this work, please cite the following:

@inproceedings{hayatSHGLNN2023, 
    author = {Hayat, Malik Khizar, Xue, Shan and Yang, Jian},
    title = {Self-supervised Heterogeneous Hypergraph Learning with Context-aware Pooling for Graph-level Classification},
    booktitle = {ICDM},
    year = {2023},
    pages={140-149},
    doi={10.1109/ICDM58522.2023.00023}
}