Skip to content

[IPDPS 2024] Adaptive neighbor sampling for temporal GNN

License

Notifications You must be signed in to change notification settings

facebookresearch/taser-tgnn

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning

Setup

  1. Setup a Python environment (>=3.11). Install PyTorch (>=2.0.1) and Deep Graph Library (>=1.1).

  2. Install nvcc for cuda compilation. Make sure to choose the compatible cuda version with your PyTorch.

        conda install cuda -c nvidia/label/cuda-11.8.0
    
  3. Build temporal_sampling GPU operator

        cd src/temporal_sampling/
        python setup.py build_ext --inplace
    

Download and Preprocess Dataset

  1. Download datasets to the DATA/ folder.

    Note: A preprocessed version of the datasets we used is available on Google Drive. If you use this version, you can skip the preprocessing steps below.

  2. Convert edge CSV to the Temporal-CSR format

        python src/gen_graph.py --data WIKI
    
  3. Preprocess negative edges

        python src/preprocess.py --data WIKI --clip_root_set
    

TASER+TGNN co-training

    python src/train.py --config config_train/tgat_wiki/TGAT.yml \
                        --data WIKI \
                        --gpu 0 \
                        --cache \
                        --cached_ratio 0.2  

Important Arguments:

  • --config: Config of TASER+TGNN reported in the paper. The configs of other datasets/models are under the config_train folder.
  • --data: The training datasets. Available choices [WIKI, REDDIT, Flight, MovieLens, GDELT]
  • --cache: Enable GPU caching
  • --cached_ratio: Ratios of node features cached in GPU.

License

TASER is MIT licensed, as found in the LICENSE file.

About

[IPDPS 2024] Adaptive neighbor sampling for temporal GNN

Topics

Resources

License

Code of conduct

Security policy

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •