This repository contains the source code for the paper UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks, accepted by IJCAI 2021.
If you find this work useful in your research, please consider cite:
@inproceedings{ijcai21-UniGNN,
title = {UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks},
author = {Huang, Jing and Yang, Jie},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
year = {2021}
}Our code requires Python>=3.6.
We recommed using virtual environtment and install the newest versions of Pytorch and Pytorch-Geometric.
You also need these additional packages:
- scipy
- path
- tqdm
Please download the datasets from HyperGCN and copy the data directory into this repository.
Baseline results can be found in HyperSAGE, HyperGCN or MPNN-R.
python train.py --data=coauthorship --dataset=dblp --model-name=UniSAGE You should probably see final accuracies like the following.
Average final test accuracy: 88.53697896003723 ± 0.21541351159170083
python train_val.py --data=cocitation --dataset=cora --use-norm --add-self-loop --model-name=UniGCNII --nlayer=32 --dropout=0.2 --patience=150 --epochs=1000 --n-runs=1You should probably see final accuracies like the following. We report the performance of the model with the highest validation score.
best test accuracy: 72.92, acc(last): 73.16
python train_evolving.py --data=coauthorship --dataset=dblp --model-name=UniGIN You should probably see final accuracies like the following.
Average final seen: 89.47043359279633 ± 0.23909984894330719
Average final unseen: 82.99945294857025 ± 0.40471906653645956
usage: UniGNN: Unified Graph and Hypergraph Message Passing Model
[-h] [--data DATA] [--dataset DATASET] [--model-name MODEL_NAME]
[--first-aggregate FIRST_AGGREGATE]
[--second-aggregate SECOND_AGGREGATE] [--add-self-loop] [--use-norm]
[--activation ACTIVATION] [--nlayer NLAYER] [--nhid NHID]
[--nhead NHEAD] [--dropout DROPOUT] [--input-drop INPUT_DROP]
[--attn-drop ATTN_DROP] [--lr LR] [--wd WD] [--epochs EPOCHS]
[--n-runs N_RUNS] [--gpu GPU] [--seed SEED] [--patience PATIENCE]
[--nostdout] [--split SPLIT] [--out-dir OUT_DIR]
optional arguments:
-h, --help show this help message and exit
--data DATA data name (coauthorship/cocitation) (default:
coauthorship)
--dataset DATASET dataset name (e.g.: cora/dblp for coauthorship,
cora/citeseer/pubmed for cocitation) (default: cora)
--model-name MODEL_NAME
UniGNN Model(UniGCN, UniGAT, UniGIN, UniSAGE...)
(default: UniSAGE)
--first-aggregate FIRST_AGGREGATE
aggregation for hyperedge h_e: max, sum, mean
(default: mean)
--second-aggregate SECOND_AGGREGATE
aggregation for node x_i: max, sum, mean (default:
sum)
--add-self-loop add-self-loop to hypergraph (default: False)
--use-norm use norm in the final layer (default: False)
--activation ACTIVATION
activation layer between UniConvs (default: relu)
--nlayer NLAYER number of hidden layers (default: 2)
--nhid NHID number of hidden features, note that actually it's
#nhid x #nhead (default: 8)
--nhead NHEAD number of conv heads (default: 8)
--dropout DROPOUT dropout probability after UniConv layer (default: 0.6)
--input-drop INPUT_DROP
dropout probability for input layer (default: 0.6)
--attn-drop ATTN_DROP
dropout probability for attentions in UniGATConv
(default: 0.6)
--lr LR learning rate (default: 0.01)
--wd WD weight decay (default: 0.0005)
--epochs EPOCHS number of epochs to train (default: 200)
--n-runs N_RUNS number of runs for repeated experiments (default: 10)
--gpu GPU gpu id to use (default: 0)
--seed SEED seed for randomness (default: 1)
--patience PATIENCE early stop after specific epochs (default: 200)
--nostdout do not output logging to terminal (default: False)
--split SPLIT choose which train/test split to use (default: 1)
--out-dir OUT_DIR output dir (default: runs/test)
Distributed under the MIT License. See LICENSE for more information.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent