This repository is the implementation of SELAR.

Dasol Hwang^* , Jinyoung Park^* , Sunyoung Kwon, Kyung-min Kim, Jung-Woo Ha, Hyunwoo J. Kim, Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs, In Advanced in Neural Information Processing Systems (NeurIPS 2020).

We used datasets from KGNN-LS and RippleNet for link prediction. Download meta-paths label (meta_labels/) from this link.

• data/music/

  • ratings_final.npy : preprocessed rating file released by KGNN-LS;
  • kg_final.npy : knowledge graph file;
    • meta_labels/
      • pos_meta{}_{}.pickle : meta-path positive label for auxiliary task
      • neg_meta{}_{}.pickle : meta-path negative label for auxiliary task

• data/book/

  • ratings_final.npy : preprocessed rating file released by RippleNet;
  • kg_final.npy : knowledge graph file;
    • meta_labels/
      • pos_meta{}_{}.pickle : meta-path positive label for auxiliary task
      • neg_meta{}_{}.pickle : meta-path negative label for auxiliary task

A list of dependencies will need to be installed in order to run the code. We provide the dependency yaml file (env.yml)

$ conda env create -f env.yml

# check optional arguments [-h]
$ python main_music.py
$ python main_book.py

@article{hwang2020self,
  title={Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs},
  author={Hwang, Dasol and Park, Jinyoung and Kwon, Sunyoung and Kim, KyungMin and Ha, Jung-Woo and Kim, Hyunwoo J},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

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