HyperGCN

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.

Dependencies

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

Training

To train the model, type the following command:

python main.py --mediators True --split 1 --data coauthorship -- dataset dblp

It 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

Experimental setting

Datasets

We use the following datasets for the experiments.

• coauthorship : dblp, cora
• cocitation : citeseer, cora, pubmed

Default Hyperparameters

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

Difference between this and the original repository

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).

The experimental result of the paper

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

The reproduced result

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

Hyperparameter tuning

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

References

[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.