wehub-resource-sync
77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
# LINE Example
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- Paper link: [here](https://arxiv.org/pdf/1503.03578)
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- Official implementation: [here](https://github.com/tangjianpku/LINE)
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This implementation includes both LINE-1st and LINE-2nd. The detailed usage is shown in the arguments in line.py.
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## How to load ogb data
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To load ogb dataset, you need to run the following command, which will output a network file, ogbn-products-net.txt:
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```
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python3 load_dataset.py --name ogbn-proteins
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```
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Or you can run the code directly with:
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```
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python3 line.py --ogbn_name xxx --load_from_ogbn
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```
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However, ogb.nodeproppred might not be compatible with mixed training with multi-gpu. If you want to do mixed training, please use no more than 1 gpu by the command above. We leave the commands to run with multi-gpu at the end.
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## Evaluation
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For evaluatation we follow the code mlp.py provided by ogb [here](https://github.com/snap-stanford/ogb/blob/master/examples/nodeproppred/).
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## Used config
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ogbn-arxiv
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```
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python3 line.py --save_in_pt --dim 128 --lap_norm 0.1 --mix --gpus 0 --batch_size 1024 --output_emb_file arxiv-embedding.pt --num_samples 1000 --print_interval 1000 --negative 5 --fast_neg --load_from_ogbn --ogbn_name ogbn-arxiv
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cd ./ogb/blob/master/examples/nodeproppred/arxiv
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cp embedding_pt_file_path ./
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python3 mlp.py --device 0 --use_node_embedding
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```
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ogbn-proteins
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```
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python3 line.py --save_in_pt --dim 128 --lap_norm 0.01 --mix --gpus 1 --batch_size 1024 --output_emb_file protein-embedding.pt --num_samples 600 --print_interval 1000 --negative 1 --fast_neg --load_from_ogbn --ogbn_name ogbn-proteins --print_loss
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cd ./ogb/blob/master/examples/nodeproppred/proteins
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cp embedding_pt_file_path ./
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python3 mlp.py --device 0 --use_node_embedding
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```
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ogbl-products
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```
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python3 line.py --save_in_pt --dim 128 --lap_norm 0.01 --mix --gpus 0 --batch_size 4096 --output_emb_file products-embedding.pt --num_samples 3000 --print_interval 1000 --negative 1 --fast_neg --load_from_ogbn --ogbn_name ogbn-products --print_loss
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cd ./ogb/blob/master/examples/nodeproppred/products
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cp embedding_pt_file_path ./
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python3 mlp.py --device 0 --use_node_embedding
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```
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## Results
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ogbn-arxiv
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<br>#params: 33023343(model) + 142888(mlp) = 33166231
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<br>Highest Train: 82.94 ± 0.11
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<br>Highest Valid: 71.76 ± 0.08
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<br>Final Train: 80.74 ± 1.30
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<br>Final Test: 70.47 ± 0.19
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<br>obgn-proteins
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<br>#params: 25853524(model) + 129648(mlp) = 25983172
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<br>Highest Train: 93.11 ± 0.04
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<br>Highest Valid: 70.50 ± 1.29
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<br>Final Train: 77.66 ± 10.27
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<br>Final Test: 62.07 ± 1.25
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<br>ogbn-products
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<br>#params: 477570049(model) + 136495(mlp) = 477706544
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<br>Highest Train: 98.01 ± 0.32
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<br>Highest Valid: 89.57 ± 0.09
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<br>Final Train: 94.96 ± 0.43
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<br>Final Test: 72.52 ± 0.29
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## Notes
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To utlize multi-GPU training, we need to load datasets as a local file before training by the following command:
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```
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python3 load_dataset.py --name dataset_name
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```
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where `dataset_name` can be `ogbn-arxiv`, `ogbn-proteins`, and `ogbn-products`. After that, a local file `$dataset_name$-graph.bin` will be generated. Then run:
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```
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python3 line.py --data_file $dataset_name$-graph.bin
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```
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where the other parameters are the same with used configs without using `--load_from_ogbn` and `--ogbn_name`. |