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
59 Dec 14, 2022
24 Oct 01, 2022
2 Jul 09, 2022
101 Dec 05, 2022
13k Jan 09, 2023
257 Dec 31, 2022
288 Oct 28, 2022
146 Dec 20, 2022
13 Dec 07, 2022
11 Oct 07, 2022
19 Oct 19, 2022
189 Dec 28, 2022
120 Dec 12, 2022
2 Oct 21, 2021
3 Oct 09, 2022
91 Dec 26, 2022
5 Nov 03, 2022
1.7k Dec 31, 2022
49 Dec 27, 2022
26 May 24, 2022