MARIOH: Multiplicity-Aware Hypergraph Reconstruction

This repository provides the official implementation of MARIOH, a supervised method for reconstructing hyperedges in hypergraphs by leveraging edge multiplicity. MARIOH integrates several key components: a theoretically guaranteed filtering step to identify true size-2 hyperedges, a multiplicity-aware classifier for scoring hyperedge candidates, and a bidirectional search strategy that explores both high- and low-confidence cliques. These components work together to achieve accurate and efficient hypergraph reconstruction. For further details, please refer to our accompanying research paper.

Contents

• main.py: The entry point script to run the hyperedge reconstruction pipeline.
• params.py: Parameter dictionaries for various datasets and modes (reduced or preserved).
• utils/: A directory containing modularized code for data processing, feature extraction, graph operations, model training, evaluation, and input/output utilities.
• data/: A directory that should contain the dataset-specific training and testing files.

Requirements

• Python Version: 3.8+ recommended

• Dependencies:

  • numpy
  • networkx
  • torch
  • joblib
  • argparse
  • Additional Python dependencies can be installed via:

    pip install -r requirements.txt

  Adjust requirements.txt or the installation commands as needed for your environment.

Datasets

You must place your datasets into the data/ directory. Each dataset should have its own subdirectory, for example:

data/
|-- {dataset_name}/
    |-- train.txt        # Training data (reduced mode)
    |-- test.txt         # Testing data (reduced mode)
    |-- train_dup.txt    # Training data (preserved mode)
    +-- test_dup.txt     # Testing data (preserved mode)

• Reduced mode uses train.txt and test.txt.
• Preserved mode uses train_dup.txt and test_dup.txt.

Please refer to the related publication for details on dataset formats and preprocessing steps.

Running the Code

To run the pipeline, navigate to the directory containing main.py and execute:

python main.py --data {dataset_name} --gpu 0 --seed 42 --output_dir output

Arguments

• --data {dataset_name}: Specify the dataset folder name located under data/.
• --gpu {int}: GPU device number. If no GPU is available or you wish to run on CPU, set --gpu to a non-existent GPU ID (e.g., --gpu 99), and it will default to CPU.
• --seed {int}: Random seed for reproducibility.
• --output_dir {path}: Directory to store the output hyperedge predictions and results.
• --preserved: Optional flag. If set, the pipeline will run in "preserved" mode using train_dup.txt and test_dup.txt. If omitted, the pipeline runs in "reduced" mode using train.txt and test.txt.

Examples

Reduced mode (default):

python main.py --data hschool --gpu 0 --seed 123 --output_dir output

Preserved mode:

python main.py --data hschool --gpu 0 --seed 123 --output_dir output --preserved

In these examples, the code will:

1. Load and preprocess the graph data.
2. Extract features and prepare a training dataset.
3. Train a classifier network with the best parameters specified in params.py.
4. Use the trained classifier to reconstruct hyperedges in the test graph.
5. Save the reconstructed hyperedges to output/reconstructed_hyp_reduced/{dataset_name}_{seed}.txt (in reduced mode) or output/reconstructed_hyp_preserved/{dataset_name}_{seed}.txt (in preserved mode).

Interpreting the Results

• Output Files: The final reconstructed hyperedges are stored as comma-separated node IDs per line.
• Evaluation Metrics: The code prints evaluation metrics such as Jaccard similarity and multiset Jaccard similarity during execution. These metrics help assess the quality of hyperedge reconstruction relative to the ground truth.
• Performance & Reproducibility: By setting the random seed (--seed) and controlling hyperparameters through params.py, you can reproduce experimental results reported in the associated research paper.

Extending and Customizing

• Modify params.py to add or change hyperparameters for different datasets.
• Adjust or add dataset loaders in utils/data_processing.py if your input format differs.
• Add new evaluation metrics in utils/evaluation.py.