- Paper link: here
- Other implementation: gensim, deepwalk-c
The implementation includes multi-processing training with CPU and mixed training with CPU and multi-GPU.
- PyTorch 1.5.0+
- PyTorch 1.5.0
- DGL 0.5.0
Currently, we support two builtin dataset: youtube and blog. Use --data_file youtube to select youtube dataset and --data_file blog to select blog dataset. The data is avaliable at https://data.dgl.ai/dataset/DeepWalk/youtube.zip and https://data.dgl.ai/dataset/DeepWalk/blog.zip The youtube.zip includes both youtube-net.txt, youtube-vocab.txt and youtube-label.txt; The blog.zip includes both blog-net.txt, blog-vocab.txt and blog-label.txt.
For other datasets please pass the full path to the trainer through --data_file and the format of a network file should follow:
1(node id) 2(node id)
1 3
1 4
2 4
...
To run the code:
python3 deepwalk.py --data_file youtube --output_emb_file emb.txt --mix --lr 0.2 --gpus 0 1 2 3 --batch_size 100 --negative 5
By default the trained embedding is saved under --output_embe_file FILE_NAME as a numpy object. To save the trained embedding in raw format(txt format), please use --save_in_txt argument.
To evalutate embedding on multi-label classification, please refer to here
YouTube (1M nodes).
| Implementation | Macro-F1 (%) 1% 3% 5% 7% 9% |
Micro-F1 (%) 1% 3% 5% 7% 9% |
|---|---|---|
| gensim.word2vec(hs) | 28.73 32.51 33.67 34.28 34.79 | 35.73 38.34 39.37 40.08 40.77 |
| gensim.word2vec(ns) | 28.18 32.25 33.56 34.60 35.22 | 35.35 37.69 38.08 40.24 41.09 |
| ours | 24.58 31.23 33.97 35.41 36.48 | 38.93 43.17 44.73 45.42 45.92 |
The comparison between running time is shown as below, where the numbers in the brackets denote time used on random-walk.
| Implementation | gensim.word2vec(hs) | gensim.word2vec(ns) | Ours |
|---|---|---|---|
| Time (s) | 27119.6(1759.8) | 10580.3(1704.3) | 428.89 |
Parameters.
- walk_length = 80, number_walks = 10, window_size = 5
- Ours: 4GPU (Tesla V100), lr = 0.2, batchs_size = 128, neg_weight = 5, negative = 1, num_thread = 4
- Others: workers = 8, negative = 5
Speeding-up with mixed CPU & multi-GPU. The used parameters are the same as above.
| #GPUs | 1 | 2 | 4 |
|---|---|---|---|
| Time (s) | 1419.64 | 952.04 | 428.89 |
You can run the code directly with:
python3 deepwalk --ogbl_name xxx --load_from_ogbl
However, ogb.linkproppred 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.
For evaluatation we follow the code mlp.py provided by ogb here.
ogbl-collab
python3 deepwalk.py --ogbl_name ogbl-collab --load_from_ogbl --save_in_pt --output_emb_file collab-embedding.pt --num_walks 50 --window_size 2 --walk_length 40 --lr 0.1 --negative 1 --neg_weight 1 --lap_norm 0.01 --mix --gpus 0 --num_threads 4 --print_interval 2000 --print_loss --batch_size 128 --use_context_weight
cd ./ogb/blob/master/examples/linkproppred/collab/
cp embedding_pt_file_path ./
python3 mlp.py --device 0 --runs 10 --use_node_embedding
ogbl-ddi
python3 deepwalk.py --ogbl_name ogbl-ddi --load_from_ogbl --save_in_pt --output_emb_file ddi-embedding.pt --num_walks 50 --window_size 2 --walk_length 80 --lr 0.1 --negative 1 --neg_weight 1 --lap_norm 0.05 --only_gpu --gpus 0 --num_threads 4 --print_interval 2000 --print_loss --batch_size 16 --use_context_weight
cd ./ogb/blob/master/examples/linkproppred/ddi/
cp embedding_pt_file_path ./
python3 mlp.py --device 0 --runs 10 --epochs 100
ogbl-ppa
python3 deepwalk.py --ogbl_name ogbl-ppa --load_from_ogbl --save_in_pt --output_emb_file ppa-embedding.pt --negative 1 --neg_weight 1 --batch_size 64 --print_interval 2000 --print_loss --window_size 1 --num_walks 30 --walk_length 80 --lr 0.1 --lap_norm 0.02 --mix --gpus 0 --num_threads 4
cp embedding_pt_file_path ./
python3 mlp.py --device 2 --runs 10
ogbl-citation
python3 deepwalk.py --ogbl_name ogbl-citation --load_from_ogbl --save_in_pt --output_emb_file embedding.pt --window_size 2 --num_walks 10 --negative 1 --neg_weight 1 --walk_length 80 --batch_size 128 --print_loss --print_interval 1000 --mix --gpus 0 --use_context_weight --num_threads 4 --lap_norm 0.01 --lr 0.1
cp embedding_pt_file_path ./
python3 mlp.py --device 2 --runs 10 --use_node_embedding
ogbl-collab
#params: 61258346(model) + 131841(mlp) = 61390187
Hits@10
Highest Train: 74.83 ± 4.79
Highest Valid: 40.03 ± 2.98
Final Train: 74.51 ± 4.92
Final Test: 31.13 ± 2.47
Hits@50
Highest Train: 98.83 ± 0.15
Highest Valid: 60.61 ± 0.32
Final Train: 98.74 ± 0.17
Final Test: 50.37 ± 0.34
Hits@100
Highest Train: 99.86 ± 0.04
Highest Valid: 66.64 ± 0.32
Final Train: 99.84 ± 0.06
Final Test: 56.88 ± 0.37
obgl-ddi
#params: 1444840(model) + 99073(mlp) = 1543913
Hits@10
Highest Train: 33.91 ± 2.01
Highest Valid: 30.96 ± 1.89
Final Train: 33.90 ± 2.00
Final Test: 15.16 ± 4.28
Hits@20
Highest Train: 44.64 ± 1.71
Highest Valid: 41.32 ± 1.69
Final Train: 44.62 ± 1.69
Final Test: 26.42 ± 6.10
Hits@30
Highest Train: 51.01 ± 1.72
Highest Valid: 47.64 ± 1.71
Final Train: 50.99 ± 1.72
Final Test: 33.56 ± 3.95
ogbl-ppa
#params: 150024820(model) + 113921(mlp) = 150138741
Hits@10
Highest Train: 4.78 ± 0.73
Highest Valid: 4.30 ± 0.68
Final Train: 4.77 ± 0.73
Final Test: 2.67 ± 0.42
Hits@50
Highest Train: 18.82 ± 1.07
Highest Valid: 17.26 ± 1.01
Final Train: 18.82 ± 1.07
Final Test: 17.34 ± 2.09
Hits@100
Highest Train: 31.29 ± 2.11
Highest Valid: 28.97 ± 1.92
Final Train: 31.28 ± 2.12
Final Test: 28.88 ± 1.53
ogbl-citation
#params: 757811178(model) + 131841(mlp) = 757943019
MRR
Highest Train: 0.9381 ± 0.0003
Highest Valid: 0.8469 ± 0.0003
Final Train: 0.9377 ± 0.0004
Final Test: 0.8479 ± 0.0003
For efficiency, the results of ogbl-collab, ogbl-ppa, ogbl-ddi are run with multi-GPU. Since ogb is somehow incompatible with our multi-GPU implementation, we need to do some preprocessing. The command is:
python3 load_dataset.py --name dataset_name
It will output a data file to the local. For example, if dataset_name is ogbl-collab, then a file ogbl-collab-net.txt will be generated. Then we run
python3 deepwalk.py --data_file data_file_path
where the other parameters are the same with used configs without using --load_from_ogbl and --ogbl_name.
The performance on ogbl-ddi and ogbl-ppa can be not that stable.