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tch-geometric

License CI PyPI version

Pytorch Geometric extension library

FeaturesInstallationExamples

Features

Pytorch Geometric extension library with additional graph sampling algorithms.

Supports:

  • Node2Vec 1 (random_walk)
  • Temporal Random Walk (temporal_random_walk)
  • Biased Temporal Random Walk (CTDNE) 2 (biased_tempo_random_walk)
  • Negative Sampling (negative_sample_neighbors_homogenous and negative_sample_neighbors_heterogenous)
  • GraphSAGE + budget sampling (budget_sampling)
  • Temporal Heterogenous Graph Transformer (HGT) sampling [^4] (hgt_sampling)
  • GraphSAGE 3 (neighbor_sampling_heterogenous and neighbor_sampling_homogenous)

Installation

CPU only

If you are using CPU only installation of pytoch, install tch-geometric using pip:

pip install tch_geometric

GPU

If you are using GPU accelerated pytoch you need to use conda

conda install egordm::tch_geometric

You can specify the cuda version explicity as follows (python 3.9, pytorch 1.11, cudnn 113):

conda install egordm::tch_geometric=0.1.0=py39_torch_1.11.0_cu113

Examples

THe examples can be found in the examples folder

Footnotes

  1. A. Grover and J. Leskovec, “node2vec: Scalable Feature Learning for Networks,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, Aug. 2016, pp. 855–864. doi: 10.1145/2939672.2939754.

  2. G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim, “Continuous-Time Dynamic Network Embeddings,” in Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18, Lyon, France, 2018, pp. 969–976. doi: 10.1145/3184558.3191526.

  3. W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, Dec. 2017, pp. 1025–1035. [^4] Z. Hu, Y. Dong, K. Wang, and Y. Sun, “Heterogeneous Graph Transformer (tempo),” arXiv:2003.01332 [cs, stat], Mar. 2020, Accessed: Mar. 09, 2022. [Online]. Available: http://arxiv.org/abs/2003.01332