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Datasets:
Cloudy1225
/
HTAG

Tasks:
Graph Machine Learning
Text Classification
Languages:
English
Size:
1M<n<10M
License:
Dataset card Data Studio Files Community
HTAG / README.md
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metadata
license: mit
arxiv: 2412.08937
task_categories:
  - graph-ml
  - text-classification
language:
  - en
size_categories:
  - 1M<n<10M

Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains

  • Multi Scales. Our HTAG datasets span multiple scales, ranging from small (24K nodes, 104K edges) to large (5.6M nodes, 29.8M edges). Smaller datasets are suitable for testing computationally intensive algorithms, while larger datasets, such as DBLP and Patent, support the development of scalable models that leverage mini-batching and distributed training.
  • Diverse Domains. Our HTAG datasets include heterogeneous graphs that are representative of a wide range of domains: movie collaboration, community question answering, academic, book publication, and patent application. The broad coverage of domains empowers the development and demonstration of graph foundation models and helps differentiate them from domain-specific approaches.
  • Realistic and Reproducible Evaluation. We provide an automated evaluation pipeline for HTAGs that streamlines data processing, loading, and model evaluation, ensuring seamless reproducibility. Additionally, we employ time-based data splits for each dataset, which offer a more realistic and meaningful evaluation compared to traditional random splits.
  • Open-source Code for Dataset Construction. We have released the complete code for constructing our HTAG datasets, allowing researchers to build larger and more complex heterogeneous text-attribute graph datasets. For example, the CroVal dataset construction code can be used to create web-scale community question-answering networks, such as those derived from StackExchange data dumps. This initiative aims to further advance the field by providing the tools necessary for replicating and extending our datasets for a wide range of applications.

Download 

from huggingface_hub import snapshot_download

# Download all
snapshot_download(repo_id="Cloudy1225/HTAG", repo_type="dataset", local_dir="./data")

# Or just download heterogeneous graphs and PLM-based node features
snapshot_download(repo_id="Cloudy1225/HTAG", repo_type="dataset", local_dir="./data", allow_patterns="*.pkl")

# Or just download raw texts
snapshot_download(repo_id="Cloudy1225/HTAG", repo_type="dataset", local_dir="./data", allow_patterns=["*.csv", "*.csv.zip"])

Dataset Format

The dataset includes heterogeneous graph edges, raw text, PLM-based features, labels, and years associated with text-attributed nodes. Raw text is provided in .csv or .csv.zip files, while the remaining data are stored in a dictionary object within a .pkl file. For example, by reading the tmdb.pkl file, the following dictionary can be obtained:

{'movie-actor': (array([   0,    0,    0, ..., 7504, 7504, 7504], dtype=int16),
  array([    0,     1,     2, ..., 11870,  1733, 11794], dtype=int16)),
 'movie-director': (array([   0,    0,    0, ..., 7503, 7503, 7504], dtype=int16),
  array([   0,    1,    2, ..., 3423,  966, 2890], dtype=int16)),
 'movie_labels': array([3, 1, 1, ..., 1, 1, 2], dtype=int8),
 'movie_feats': array([[ 0.00635284,  0.00649689,  0.01250827, ...,  0.06342042,
         -0.01747945,  0.0134356 ],
        [-0.14075027,  0.02825641,  0.02670695, ..., -0.12270895,
          0.08417314,  0.02486392],
        [ 0.00014208, -0.02286632,  0.00615967, ..., -0.03311544,
          0.04735276, -0.07458566],
        ...,
        [ 0.01835816,  0.07484645, -0.08099765, ..., -0.00150019,
          0.01669764,  0.00456595],
        [-0.00821487, -0.10434289,  0.01928608, ..., -0.06343049,
          0.05060194, -0.04229118],
        [-0.06465845,  0.13461556, -0.01640793, ..., -0.06274845,
          0.04002513, -0.00751513]], dtype=float32),
 'movie_years': array([2013, 1995, 1989, ..., 1939, 1941, 1965], dtype=int16)}

Dataset Statistics

# Nodes # Edges # Classes # Splits
TMDB 24,412 104,858 4 Train: 5,698
Movie: 7,505 Movie-Actor: 86,517 Valid: 711
Actor: 13,016 Movie-Director: 18,341 Test: 1,096
Director: 3,891
CroVal 44386 164,981 6 Train: 980
Question: 34153 Question-Question: 46,269 Valid: 1,242
User: 8898 Question-User: 34,153 Test: 31,931
Tag: 1335 Question-Tag: 84,559
ArXiv 231,111 2,075,692 40 Train: 47,084
Paper: 81,634 Paper-Paper: 1,019,624 Valid: 18,170
Author: 127,590 Paper-Author: 300,233 Test: 16,380
FoS: 21,887 Paper-FoS: 755,835
Book 786,257 9,035,291 8 Train: 330,201
Book Book-Book: 7,614,902 Valid: 57,220
Author Book-Author: 825,905 Test: 207,063
Publisher Book-Publisher: 594,484
DBLP 1,989,010 29,830,033 9 Train: 508,464
Paper: 964350 Paper-Paper: 16,679,526 Valid: 158,891
Author: 958961 Paper-Author: 3,070,343 Test: 296,995
FoS: 65699 Paper-FoS: 10,080,164
Patent 5,646,139 8,833,738 120 Train: 1,705,155
Patent: 2,762,187 Patent-Inventor: 6,071,551 Valid: 374,275
Inventor: 2,873,311 Patent-Examiner: 2,762,187 Test: 682,757
Examiner: 10,641

Dataset Construction

The code for dataset construction can be found in each graph_builder.ipynb file. Please see README.md in each subfolder for more details .