This is the implementation for the work "T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations", submitted to IEEE Transactions on Neural Networks and Learning Systems
If you find this work useful in your research, please consider citing:
@article{wang2024t,
title={T-HyperGNNs: Hypergraph neural networks via tensor representations},
author={Wang, Fuli and Pena-Pena, Karelia and Qian, Wei and Arce, Gonzalo R},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2024},
publisher={IEEE}
}Our code requires Python>=3.9.
You also need these additional packages:
Please download the datasets from GoogleDrive and copy the dataset directory into this repository.
usage: python train.py
[--data_type DATA_TYPE] [--dataset DATASET] [--hyperG_norm HYPERG_NORM]
[--model MODEL] [--self_loop SELF_LOOP] [--num_layers NUM_LAYERS]
[--hid_dim HID_DIM] [--dropout DROPOUT] [--layernorm LAYERNORM]
[--batchnorm BATCHNORM] [--lr LR] [--wd WD] [--train_ratio TRAIN_RATIO]
[--valid_ratio VALID_RATIO] [--epochs EPOCHS] [--num_exps NUM_EXPS]
[--cuda CUDA] [--seed SEED] [--combine COMBINE] [--M M] [--Mlst MLST]
optional arguments:
--data_type DATA_TYPE data type (coauthorship/cocitation/3dObject/new) (default: new)
--dataset DATASET dataset name (e.g.: cora/dblp for coauthorship,
cora/citeseer/pubmed for cocitation, ModelNet40/NTU for 3dObject,
House/Walmart for new) (default: House)
--hyperG_norm HYPERG_NORM whether normalize hypergraph adjacency tensor (default: False)
--model MODEL T-HyperGNN Model(T-Spectral, T-Spatial, T-MPHN) (default: T-MPHN)
--self_loop SELF_LOOP whether add self-loop to hypergraph (default: False)
--num_layers NUM_LAYERS number of HyperGNN layers (default: 1)
--hid_dim HID_DIM the dimension of embeddings at the hidden layer (default: 256)
--dropout DROPOUT dropout probability after UniConv layer (default: 0.6)
--layernorm LAYERNORM whether use layer normalization (default: True)
--batchnorm BATCHNORM whether use batch normalization (default: True)
--lr LR learning rate (default: 0.001)
--wd WD weight decay (default: 0.0005)
--train_ratio TRAIN_RATIO ratio of training data (default: 0.5)
--valid_ratio VALID_RATIO ratio of validation data (default: 0.25)
--epochs EPOCHS number of epochs to train (default: 100)
--num_exps NUM_EXPS number of runs for repeated experiments (default: 10)
--cuda CUDA cuda id to use (default: 0)
--seed SEED seed for randomness (default: 1)
--combine COMBINE the combine operation in T-MPHN (e.g., concat, sum))
--M M the maximum cardinality of the hypergraph (default: 3)
--Mlst MLST the maximum cardinality of the hypergraph at each layer, max(Mlst) = M (default: [3])Distributed under the MIT License. See LICENSE for more information.