MPLP: Metapath-Based Label Propagation for Heterogenous Graphs
Results on MAG240M
Here, we demonstrate the following performance on the MAG240M dataset from [email protected] 2021.
| Model | Test Acc | Validation Acc | Parameters | Hardware |
|---|---|---|---|---|
| Our Model | 0.7447 | 0.7669 ± 0.0003 (ensemble 0.7696) | 743,449 | Tesla V100 (21GB) |
Reproducing results
0. Requirements
Here just list python3 packages we used in this competition:
numpy==1.19.2
torch==1.5.1+cu101
dgl-cu101==0.6.0.post1
ogb==1.3.1
sklearn==0.23.2
tqdm==4.46.1
1. Prepare Graph and Features
The preprocess code modifed from dgl baseline. We created graph with 6 different edge types instead of 5.
# Time cost: 3hours,30mins
python3 $MAG_CODE_PATH/preprocess.py
--rootdir $MAG_INPUT_PATH \
--author-output-path $MAG_PREP_PATH/author.npy \
--inst-output-path $MAG_PREP_PATH/inst.npy \
--graph-output-path $MAG_PREP_PATH \
--graph-as-homogeneous \
--full-output-path $MAG_PREP_PATH/full_feat.npy
The graphs and features will be saved in MAG_PREP_PATH , where the MAG_PREP_PATH is specified in run.sh.
Calculate features
The meta-path based features are generated by this script. Details can be found in our technical report.
# Time cost: 2hours,20mins (only generate label related features)
python3 $MAG_CODE_PATH/feature.py
$MAG_INPUT_PATH \
$MAG_PREP_PATH/dgl_graph_full_heterogeneous_csr.bin \
$MAG_FEAT_PATH \
--seed=42
Train RGAT model and prepare RGAT features
The RGAT model is modifed from dgl baseline. The validation accuracy is 0.701 , as same as described in the dgl baseline github.
# Time cost: 33hours,40mins (20mins for each epoch)
python3 $MAG_CODE_PATH/rgat.py
--rootdir $MAG_INPUT_PATH \
--graph-path $MAG_PREP_PATH/dgl_graph_full_homogeneous_csc.bin \
--full-feature-path $MAG_PREP_PATH/full_feat.npy \
--output-path $MAG_RGAT_PATH/ \
--epochs=100 \
--model-path $MAG_RGAT_PATH/model.pt \
--submission-path $MAG_RGAT_PATH/
You will get embeddings as input features of the following MPLP models.
2. Train MPLP models
The train process splits to two steps:
- train the model with full train samples at a large learning rate (here we use StepLR(lr=0.01, step_size=100, gamma=0.25))
- then fine tune the model with latest train samples (eg, paper with year >= 2018) with a small learning rate (0.000625)
You can train the MPLP model by running the following commands:
# Time cost: 2hours,40mins for each seed
for seed in $(seq 0 7);
do
python3 $MAG_CODE_PATH/mplp.py \
$MAG_INPUT_PATH \
$MAG_MPLP_PATH/data/ \
$MAG_MPLP_PATH/output/seed${seed} \
--gpu \
--seed=${seed} \
--batch_size=10240 \
--epochs=200 \
--num_layers=2 \
--learning_rate=0.01 \
--dropout=0.5 \
--num_splits=5
done
3. Ensemble MPLP results
While having all the results with k-fold cross validation training under 8 different seeds, you can average the results by running code below:
python3 $MAG_CODE_PATH/ensemble.py $MAG_MPLP_PATH/output/ $MAG_SUBM_PATH
3 Jun 02, 2022
57 Nov 17, 2022
619 Dec 14, 2022
36 Aug 06, 2022
19 Feb 12, 2022
173 Dec 26, 2022
108 Dec 25, 2022
32 Nov 21, 2022
17 Jan 03, 2023
66 Nov 24, 2022
838 Jan 01, 2023
1 Dec 18, 2021
8 Jul 13, 2022
40 Sep 18, 2022
7 Jul 21, 2022
220 Jan 07, 2023
10 Dec 06, 2022
49 Oct 24, 2022
1 Jan 17, 2022
10 Dec 08, 2022