This repository aims to reproduce HyperGCN proposed in the paper entitled "HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs (NeurIPS 2019)". We refer to the original repository of HyperGCN to implement this repository.
We use the following Python packages to implement this. Install them in your environment using pip install -r requirements.txt.
- tqdm == 4.62.1
- torch == 1.9.0
- scipy == 1.7.1
- numpy == 1.21.2
- ConfigArgParse == 1.5.2
To train the model, type the following command:
python main.py --mediators True --split 1 --data coauthorship -- dataset dblpIt will train the model for the node classification task in the given hypergraph.
In this project, you can control the value of each hyperparameter in config.py.
The details of the hyperparameters are in the following table.
| Option | Description | Default |
|---|---|---|
| data | coauthorship/cocitation | coauthorship |
| dataset | cora/dblp for coauthorship, cora/citeseer/pubmed for cocitation | dblp |
| mediators | use of Laplacian | False |
| fast | use of FastHyperGCN | False |
| split | train-test split dataset | 1 |
| gpu | gpu number to use | 3 |
| cuda | True or False | True |
| seed | and integer for random | 5 |
| depth | number of hidden layers | 2 |
| dropout | dropout probability for hidden layers | 0.5 |
| epochs | number of training epochs | 200 |
| rate | learning rate | 0.01 |
| decay | weight decay | 0.0005 |
| model | model for hypergraph | HyperGCN |
We use the following datasets for the experiments.
- coauthorship : dblp, cora
- cocitation : citeseer, cora, pubmed
The followings are default hyperparameters, as described in the original repository.
- hidden layer size : 32
- dropout rate : 0.5
- learning rate : 0.01
- weight decay : 0.0005
- training epochs : 200
- lamda for explicit Laplacian regularisation : 0.001
The above heperparameter setting suggested by the originaly repository seems to have an issue to produce the following results reported in its paper (the related issue is found here).
| Method | DBLP co-authorship |
Pubmed co-citation |
Cora co-authorship |
Cora co-citation |
Citeseer co-citation |
|---|---|---|---|---|---|
| 1-HyperGCN | 33.87 ± 2.4 | 30.08 ± 1.5 | 36.22 ± 2.2 | 34.45 ± 2.1 | 38.87 ± 1.9 |
| FastHyperGCN | 27.34 ± 2.1 | 29.48 ± 1.6 | 32.54 ± 1.8 | 32.43 ± 1.8 | 37.42 ± 1.7 |
| HyperGCN | 24.09 ± 2.0 | 25.56 ± 1.6 | 30.08 ± 1.8 | 32.37 ± 1.7 | 37.35 ± 1.6 |
| Method | DBLP co-authorship |
Pubmed co-citation |
Cora co-authorship |
Cora co-citation |
Citeseer co-citation |
|---|---|---|---|---|---|
| 1-HyperGCN | 32.64 ± 9.3 | 35.54 ± 15.4 | 48.98 ± 10.1 | 52.02 ± 12.2 | 44.65 ± 10.2 |
| FastHyperGCN | 30.20 ± 12.6 | 44.71 ± 13.3 | 49.82 ± 9 | 48.66 ± 15.6 | 50.11 ± 9.8 |
| HyperGCN | 27.28 ± 8.1 | 37.36 ± 16.8 | 52.19 ± 14.4 | 43.33 ± 12.2 | 51.77 ± 9.1 |
We think that the problematic hyperparameters are learning rate = 0.01 and epochs = 200.
To find better hyperparameters, we perform grid searches for learning rate and epochs as follows:
- Split = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
- Learning rate = [0.001, 0.005, 0.01, 0.05, 0.1]
- Epoch = [100, 200, 500, 1000, 2000]
We summarize the result of this experiment in the following table. We report the average of test errors (lower is better) with their standard deviation over 10 runs (accuracy = 100 - error).
| Task | Method | Learning rate | Epochs | Error ± std |
|---|---|---|---|---|
| DBLP (coauthorship) |
1-HyperGCN | 0.1 | 2000 | 15.45 ± 0.5 |
| DBLP (coauthorship) |
HyperGCN | 0.1 | 2000 | 15.49 ± 0.3 |
| DBLP (coauthorship) |
FastHyperGCN | 0.1 | 2000 | 15.49 ± 0.3 |
| Pubmed (co_citation) |
1-HyperGCN | 0.1 | 1500 | 21.0 ± 1.2 |
| Pubmed (co_citation) |
HyperGCN | 0.1 | 1500 | 22.1 ± 1.4 |
| Pubmed (co_citation) |
FastHyperGCN | 0.1 | 1500 | 20.62 ± 1.0 |
| Cora (coauthorship) |
1-HyperGCN | 0.1 | 1000 | 34.69 ± 2.4 |
| Cora (coauthorship) |
HyperGCN | 0.1 | 1000 | 35.37 ± 1.8 |
| Cora (coauthorship) |
FastHyperGCN | 0.1 | 1000 | 33.21 ± 2.8 |
| Cora (co-citation) |
1-HyperGCN | 0.1 | 1500 | 34.69 ± 2.4 |
| Cora (co-citation) |
HyperGCN | 0.1 | 1500 | 35.47 ± 2.2 |
| Cora (co-citation) |
FastHyperGCN | 0.1 | 1500 | 35.77 ± 3.3 |
| Citeseer (co-citation) |
1-HyeprGCN | 0.1 | 2000 | 39.59 ± 2.8 |
| Citeseer (co-citation) |
HyeprGCN | 0.1 | 2000 | 38.62 ± 1.5 |
| Citeseer (co-citation) |
FastHyeprGCN | 0.1 | 2000 | 40.83 ± 3.1 |
[1] Yadati, N., Nimishakavi, M., Yadav, P., Nitin, V., Louis, A., & Talukdar, P. (2019). HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. Advances in Neural Information Processing Systems, 32, 1511-1522.