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
16 Mar 23, 2022
34 Dec 02, 2022
4 Oct 31, 2022
10 Oct 24, 2022
35 Nov 14, 2022
6 Oct 26, 2022
12 Aug 12, 2022
92 Jan 04, 2023
759 Jan 08, 2023
732 Dec 10, 2022
341 Dec 20, 2022
67 Oct 19, 2022
10 Oct 11, 2022
150 Dec 14, 2022
7 Sep 20, 2022
62 May 21, 2022
4 Nov 08, 2022
4 Mar 12, 2022
7 Dec 01, 2022
7 Jan 02, 2022