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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.