graph_star

Introduction

Information propagation is the key idea that con-tributes to the recent success of graph neural net-work on various tasks. However, there still lacksan efficient framework that is able to achieve globalmessage passing across the whole graph. In thiswork, we present graph star net (GraphStar), anovel and unified graph neural network architec-ture that achieves non-local graphical representa-tion through topological modification of the originalgraph. In particular, we propose to use a virtual“star” node to propagate global information to allreal nodes, which significantly increase the represen-tation power without increasing the model depth orbearing heavy computational complexity. Throughextensive experiments, we provide a general pictureof how the proposed topological change affects theperformance of graph neural network on the threemajor graph tasks. Specifically, we show that intro-ducing the “star” node to effectively integrate distalinformation significantly improves the performanceof link prediction task, leading to state-of-the-arts(SOTA) performances on three benchmark datasets.On graph classification task, GraphStar achievescomparable performances with the currently bestmodels on multiple benchmark datasets and showssignificantly increased stability. On the node classi-fication task, our results also indicate the superiorperformance of our model on dense graph with in-ductive setting. All these results indicate that the pro-posed topological modification method is a promis-ing strategy for improving the current graph neuralnetwork models.

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Datasets

All datasets in the above, respective classification tasks will be downloaded automatically when executing the script for the first time. Please note that all datasets are publicly obtained online.

The proposed model is evaluated on several popular public datasets including Cora, Citeseer, MUTAG, D&D, and etc. For different tasks, the adopted datasets are different.

• For node classification tasks, we evaluate on Cora, Citeseer, Pubmed, PPI, IMDB and etc.
• For link prediction tasks, we evaluate on Cora, Citeseer, Pubmed and etc.
• For graph classification tasks, we tested the following datasets: Enzymes, D&D, Proteins and MUTAG.
• For graph classification in text classification tasks, we evaluate on the following datasets: 20NG, R8, R52, Ohsumed and MR. Those datasets can be created following the Pytorch_geometry required format.

For more information regarding the datasets, please refer to the submitted paper.

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Dependencies

The script has been tested under Python 3.6.X and on Nvidia GPU Tesla V100 (with CUDA 9.0 and cuDNN 7 installed). For installing required python packages, please refer to the "requirements.txt".

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Options

Normally we have already provided most of the optimal value which we got during traning. So you don't have to change much. Any away, we still explain the main options here.

  --device                                     INT/STR GPU ID / Use CPU                               Default is `0`, use 'cpu' if no GPU is available
  --num_star                                   INT     Number of Star.                                Default is 1.
  --epochs                                     INT     Number of training epochs.                     Default is 2000.
  --lr                                         FLOAT   Adam learning rate.                            Default is 2e-4.
  --dropout                                    FLOAT   Dropout rate value.                            Default is 0.0.
  --coef_dropout                               FLOAT   Dropout for attention coefficients.            Default is 0.0.
  --num_layers                                 INT     Number of layers.                              Default is 6.
  --hidden                                     INT     Number of hidden units.                        Default is 1024.
  --heads                                      INT     Multi-head for Attention.                      Default is 4.
  --l2                                         FLOAT   Regularization.                                Default is 0.
  --residual                                   STR     Skip connections across attentional layer      Default is True.
  --residual_star                              STR     Skip connections across attentional star       Default is True.
  --layer_norm                                 STR     Layer normalization                            Default is True.
  --layer_norm_star                            STR     Layer normalization for star                   Default is True.
  --activation                                 STR     Activation methods                             Default is `elu`.
  --star_init_method                           STR     Star initialization method                     Default is `attn`.
  --additional_self_loop_relation_type         STR     As its name implied                            Default is True.
  --additional_node_to_star_relation_type      STR     As its name implied                            Default is True.
  --relation_score_function                    STR     Score function for relation                    Default is `DistMult`.
  --patience                                   INT     Number of patience                             Default is 100.
  --dataset                                    STR     Name of dataset                                Default is "".

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Testing (Run the code)

1. For node classification tasks (cora, citeseer and pubmed .etc), choose related script then try

Prepare Imdb dataset:

cd data
wget https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz && tar zxvf aclImdb_v1.tar.gz
cd ..
python utils/imdb_data_util.py

python run_transductive_nc.py --dataset=cora --num_layers=2 --heads=8 --hidden=128 --dropout=0.7 --coef_dropout=0.2 --patience=50 --l2=2e-3  --lr=1e-3 --star_init_method=mean
python run_transductive_nc.py --dataset=citeseer --num_layers=2 --heads=8 --hidden=128 --dropout=0.7 --coef_dropout=0.2 --patience=50 --l2=4e-3  --lr=1e-3 --star_init_method=mean 
python run_transductive_nc.py --dataset=pubmed --num_layers=2 --heads=8 --hidden=128 --dropout=0.7 --coef_dropout=0.2 --patience=50 --l2=1e-4  --lr=5e-3 --star_init_method=mean

python run_imdb.py
python run_ppi.py

2. For link prediction tasks (cora, citeseer and pubmed .etc), choose related script then try

python run_lp.py --dataset=pubmed --dropout=0 --hidden=512 --l2=5e-4 --num_layers=3 --cross_layer=False --patience=500 --residual=True --residual_star=True
python run_lp.py --dataset=citeseer --dropout=0 --hidden=2048 --l2=2e-4 --num_layers=3 --cross_layer=False --patience=500 --residual=True --residual_star=True
python run_lp.py --dataset=cora --dropout=0 --hidden=2048 --l2=5e-4 --num_layers=3 --cross_layer=False --patience=200 --residual=True --residual_star=True

3. For graph classification tasks (Enzymes, D&D, Proteins), choose related script then try

python run_gc.py --dataset=ENZYMES --dropout=0.2 --coef_dropout=0.2 --hidden=64 --num_layers=3 --cross_layer=False --lr=5e-4 --l2=1e-4 --max_epoch=500
python run_gc.py --dataset=PROTEINS --dropout=0.2 --coef_dropout=0.2 --hidden=512 --num_layers=3 --cross_layer=False --lr=5e-4 --l2=1e-3 --max_epoch=500
python run_gc.py --dataset=DD --dropout=0.2 --coef_dropout=0.2 --hidden=64 --num_layers=3 --cross_layer=False --lr=5e-4 --l2=1e-3 --max_epoch=500
python run_gc.py --dataset=MUTAG --dropout=0 --coef_dropout=0 --hidden=64 --num_layers=3 --cross_layer=False --lr=5e-4 --max_epoch=200

4. For text classification tasks, change DATASET value in the script, then try

python build_text_graph.py  # only need at the first time for a single dataset
python run_text_classification.py

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Plot Graph Classification Results

For graph classification tasks (D&D, Enzymes, Proteins etc.), we adopt 10-fold cross validation as metric to evaluate their performance. A script "log_plot_tools" was created to visualize their results. To use this tool, first you need to save the print out messages during training in a txt file.

If you prefer use linux terminal, you can use the tee command, like the following way.

 python run_mutag | tee log.txt

If you use JupyterHub, you can use the magic command %capture to server this purpose, as shown in following figure.

After got the log file, just run the command in a terminal, like:

   python log_plot_tools.py --log_dir=./log_file_name.txt \
    [ --show_plots=false  --save_as_pdf=true]

Performance

Node Classification (Transductive)

Models	Cora (Acc)	Citeseer (Acc)	Pubmed (Acc)
GCN	0.815	0.703	0.790
GAT	0.830	0.725	0.790
SGC	0.806	0.714	0.770
MTGAE	0.790	0.718	0.804
LGCN	0.833	0.730	0.795
GraphStar	0.821(0.012)	0.71(0.021)	0.772(0.011)

Node Classification (Inductive)

Models	PPI (F1-Micro)
GraphSage	0.612
GAT	0.973
LGCN	0.772
JK-Dense LSTM	0.500
GaAN	0.987
GraphStar	0.994(0.001)

Models	IMDB (Acc)
oh-LSTM	0.941
L Mixed	0.955
BERT large finetune UDA	0.958
GraphStar	0.960(0.001)

Graph Classification

Models	Enzymes	D&D	Proteins	Mutag
Seal-Sage	-	0.809	0.772	-
Diff Pool	0.625	0.806	0.763	-
CAPS GNN	0.574	0.754	0.763	0.867
GraphStar	0.671(0.0027)	0.796(0.005)	0.779(0.008)	0.912(0.021)

Graph Text Classification

Models	R8	R52	20NG	MR	Ohsumed
Bi-LSTM	0.963	0.905	0.732	0.777	0.493
fastText	0.961	0.928	0.794	0.751	0.557
TextGCN	0.9707	0.9356	0.8634	0.7674	0.6836
SGCN	0.9720	0.9400	0.8850	0.7590	0.6850
GraphStar	0.974(0.002)	0.950(0.003)	0.869(0.003)	0.766(0.004)	0.642(0.006)

Link Prediction

Models	Cora	Citeseer	Pubmed
MTGAE	0.946	0.949	0.944
VGAE	0.920	0.914	0.965
GraphStar	0.959(0.003)	0.977(0.003)	0.970(0.001)

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Acknowledgement

This code is based on the Pytorch_geometry's work, so we would like to thank its contributors (Fey, Matthias and Lenssen, Jan E.) here.