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 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 dataset

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.