Official PyTorch Implementation of "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs". NeurIPS 2020.

Related tags

Deep Learning machine-learning graph-neural-networks pytorch-geometric
Overview

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

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).

Data Preprocessing

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

Required packages

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

Running the code

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

Overview of the results of link prediction

Last-FM (Music)

Base GNNs Vanilla w/o MP w/ MP SELAR SELAR+Hint
GCN 0.7963 0.7899 0.8235 0.8296 0.8121
GAT 0.8115 0.8115 0.8263 0.8294 0.8302
GIN 0.8199 0.8217 0.8242 0.8361 0.8350
SGC 0.7703 0.7766 0.7718 0.7827 0.7975
GTN 0.7836 0.7744 0.7865 0.7988 0.8067

Book-Crossing (Book)

Base GNNs Vanilla w/o MP w/ MP SELAR SELAR+Hint
GCN 0.7039 0.7031 0.7110 0.7182 0.7208
GAT 0.6891 0.6968 0.7075 0.7345 0.7360
GIN 0.6979 0.7210 0.7338 0.7526 0.7513
SGC 0.6860 0.6808 0.6792 0.6902 0.6926
GTN 0.6732 0.6758 0.6724 0.6858 0.6850

Citation

@inproceedings{NEURIPS2020_74de5f91,
 author = {Hwang, Dasol and Park, Jinyoung and Kwon, Sunyoung and Kim, KyungMin and Ha, Jung-Woo and Kim, Hyunwoo J},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {10294--10305},
 publisher = {Curran Associates, Inc.},
 title = {Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs},
 url = {https://proceedings.neurips.cc/paper/2020/file/74de5f915765ea59816e770a8e686f38-Paper.pdf},
 volume = {33},
 year = {2020}
}

License

Copyright (c) 2020-present NAVER Corp. and Korea University
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Comments
  • Issue about the env.yml

    Issue about the env.yml

    Thanks for your open source! However, I found that conda env create -f env.yml, which can not run. Therefore, Can you check the file? In addition, maybe you can provide a list of dependency? Thank you!

    opened by commencement 1
  • How does neighbor_sampler work in __produce_subgraph__ function?

    How does neighbor_sampler work in __produce_subgraph__ function?

    Hi, thanks for opening source such a good job first!

    I want to know how does neighbor_sampler here work? It seems that it is a function supported by torch_cluster, but I cannot find the corresponding document. Could you give me some guides about how to use it or the document it relates to?

    Thank you a lot.

    opened by renli1024 0
  • Please tell me when this repo is ready,thanks

    Please tell me when this repo is ready,thanks

    It seems the codes are uploaded recently, I guess that the author might change something for a while. So please tell me when this repo is ready~ that would be very nice. Thanks a lot ~~

    opened by Juicechen95 0
Owner
MLV Lab (Machine Learning and Vision Lab at Korea University)
MLV Lab (Machine Learning and Vision Lab at Korea University)
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