# DeepWalk Example - Paper link: [here](https://arxiv.org/pdf/1403.6652.pdf) - Other implementation: [gensim](https://github.com/phanein/deepwalk), [deepwalk-c](https://github.com/xgfs/deepwalk-c) The implementation includes multi-processing training with CPU and mixed training with CPU and multi-GPU. ## Dependencies - PyTorch 1.5.0+ ## Tested version - PyTorch 1.5.0 - DGL 0.5.0 ## Input data 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 ... ``` ### How to run the code 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 ``` ### How to save the embedding 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. ### Evaluation To evalutate embedding on multi-label classification, please refer to [here](https://github.com/ShawXh/Evaluate-Embedding) 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 | ## OGB Dataset ### How to load ogb data 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. ### Evaluation For evaluatation we follow the code mlp.py provided by ogb [here](https://github.com/snap-stanford/ogb/blob/master/examples/linkproppred/collab/mlp.py). ### Used config 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 Results 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 ### Notes #### Multi-GPU issues 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`. #### Others The performance on ogbl-ddi and ogbl-ppa can be not that stable.