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README.md

Dataset

A dataset is related to a task, so it is a part of task.

So dataset should load not only a heterograph[DGLGraph], but also some index involving training, validation and testing. In OpenHGNN, we preprocess the feature of dataset outside of model. Specifically, we use a linear layer with bias for each node type to map all node features to a shared feature space. And for no feature nodes, we give a embedding as its feature. Refer to HeteroFeature.

Open Heterogeneous Graph Benchmark

We release a benchmark of heterogeneou graph named Open Heterogeneous Graph Benchmark (OHGB) and leaderboard for link prediction and node classification task.

NodeClassificationDataset

  • RDF_NodeCLassification
  • HIN_NodeCLassification
    • ACM
      author paper Subject Paper-Author Paper-Subject Features Train Val Test
      acm4GTN 5,912 3,025 57 9,936 3,025 1,902 600 300 2,125
      acm_han_raw 17,351 4,025 72 13,407 4,025 1,903 808 401 2,816
      acm4NSHE 7,167 4,019 60 13,407 4,019 128(Embedding from deep walk) - - -
    • academic4HetGNN
      author paper Venue Paper-Author Paper-venue Paper-paper
      academic4HetGNN 28,646 21,044 18 69,311 21,044 21,357
    • DBLP
      author paper Conf Venue Paper-Author Paper-Conf Paper-Term Train Val Test
      dblp4HAN 4,057 14,328 20 8,789 19,645 14,328 88,420 800 400 2,857
      dblp4GTN
      dblp4MAGNN 4,057 14,328 20 7,723 19,645 14,328 85,810 400 400 3257
  • IMDB
    Movie Actor Director Movie-Actor Movie-Director Train Val Test
    imdb4HAN 4,780 5,841 2,269 14,340 4,780 300 300 2,687
    imdb4GTN 4,661 5,841 2,270 13,983 4,661 300 300 2,339
    imdb4MAGNN 4,278 5,257 2,081 12,828 4,278 400 400 3,478

  • **HGBn **

    The datasets are HGB for Node Classification

    Note：The test data labels are randomly replaced to prevent data leakage issues, refer to HGB.

    In OpenHGNN, you will get the test results in ./openhgnn/output/{model_name}/. If you want to obtain test scores, you need to submit your prediction to HGB's website.

    • HGBn-ACM
    paper author subject term paper-author paper-paper paper-subject paper-term Val Test
    3025 5959 56 1902 9949 5343 3025 255619 907 2118
    • HGBn-IMDB
    movie actor director keyword actor-movie director-movie keyword-movie train test
    4932 6124 2393 7971 14779 4932 23610 1371 3202
    • HGBn-Freebase: no feature
    BOOK BUSINESS FILM LOCATION MUSIC ORGANIZATION PEOPLE SPORTS train test
    40402 7153 19427 9368 82351 2731 17641 1025 2386 5568
    • HGBn-DBLP
    author paper term venue author-paper paper-term paper-venue train test
    4057 14328 7723 20 19645 85810 14328 1217 2840
  • OGB_NodeClassification
    • ogbn-mag
      • Train 629,571 predict venue labels of all papers published before 2018
      • Validation: 64879 papers published in 2018
      • Test: 41939 papers published since 2019

LinkPredictionDataset

  • HIN_LinkPrediction
    • academic4HetGNN

  • HGBl

    The datasets are HGB for Link Prediction.

    Note：The test data labels are randomly replaced to prevent data leakage issues, refer to HGB.

    In OpenHGNN, you will get the test results in ./openhgnn/output/{model_name}/. If you want to obtain test scores, you need to submit your prediction to HGB's website.

    • HGBl-amazon

      product features product-product0 product-product1 test : product-product0 test : product-product1
      HGBl-amazon 10099 1156 76924 71735 7609 7137

    • HGBl-LastFM

      user artist tag feature user-artist user-user artist-tag test:user-artist
      HGBL-LastFM 1892 17632 1088 0 92834 25434 23253 18567

    • HGBl-PubMed

      node0 node1 node2 node3 feature node0- node0 node0-node1 node1-node1 node2-node0 node2-node1 node2-node2 node2-node3 node3-node0 node3-node1 node3-node2 test:node1-node1
      HGBL-LastFM 13168 19507 25691 2783 200 16105 25962 42637 31277 51323 62187 6297 3155 5245 798 8528
  • KG_LinkPrediction
    • 'wn18', 'FB15k', 'FB15k-237'

RecommendationDataset

  • Amazon

    (Containing rating and timestamp information)

    (Source : http://jmcauley.ucsd.edu/data/amazon/)

    Edata['rate'] in user-item edge is the rating.

    It addresses the two most common scenarios in collaborative filtering:

    • rating prediction (e.g. on a scale of 1 to 5 stars), and
    • item prediction from positive-only feedback.
    User Item View Category Brand User-Item Item-View Item-Category Item-Brand Test(20%)
    User-Item
    Amazon 6,170 2,753 3,857 22 334 195,791 5,694 5,508 2,753 39,159

  • MTWM

    user poi Sup poi-contain-spu user-buy-poi user-buy-spu user-click-poi
    MTWM 188,155 3,474 16,889 92,024 542,915 1,797,283 1,477,316

How to build a new dataset

Overview

We use dgl.heterograph as our graph data structure.

The API dgl.save_graphs and dgl.load_graphs can be used in storing graph into the local.

The Flow
  1. Process your dataset as dgl.heterograph.
  2. Store as graph.bin. Compress as dataset_name4model_name.zip
  3. Upload the zip file to s3.
  4. If the dataset is Heterogeneous Information Network, you can modify the AcademicDataset directly. Or you can refer to it building a new Class Dataset.

We give a demo to build a new dataset.