PubMed Network (GCN Benchmark)

A PubMed dataset of 19,717 nodes, 44,327 edges, 3 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. PubMed network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.

#Benchmark graph#Graph neural network#Node classification#Pubmed

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· graph.json

The full graph has 19,717 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.

19.7K
Nodes
44.3K
Edges
2.28e-04
Density
1
Components
-0.04363
Assortativity
0.0%
Reciprocity
9.3K
Bridges
Most central nodes
61730.00867
62000.00781
51140.00664
15340.00659
94020.00634
74560.00614
42230.00446
180720.00421
49850.00406
126740.00391
Content metrics

19,717 records scored · min–maxmean ± 0.675 σ (the interquartile range if the values are roughly normal)mean

labelμ 1.184 ± 0.753
min 0max 2
feature countμ 50.111 ± 15.423
min 5max 122
is test10.0% true
true 10.0%false 90.0%
4.4
Flesch–Kincaid grade
US school grade level
0.9
Automated readability index
Frequent terms
139106105100123112109162131122114116146169111196115149104101102108117121110201130120240113232103118107174147155124129134204133192202180179141156136168
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Computed over the full corpus.

Record

Catalog key
gcn_pubmed
Authors
Yang, Zhilin; Cohen, William W.; Salakhutdinov, Ruslan
Year
2016
Source paper
Revisiting Semi-Supervised Learning with Graph Embeddings
DOI / arXiv
10.48550/arXiv.1603.08861
License
Academic Use (LicenseRef-Research-Only)
Records
19,717
Nodes
19,717
Edges
44,327
Nodes are
paper
Edges are
citation
Attributes
nodes have no attributes; edges have no attributes
Languages
en
Collected
2016 (2010s)
Tasks
Node classification (multi-class; label) inferred
From the graph: link prediction, community detection

Provenance

Originally published at
https://github.com/kimiyoung/planetoid link checked
Hosted copy
Mirrored on echo.directory on 2026-10-16, 761.3 MB across 80 files. Checksums in MANIFEST.json.

How to cite

BibTeX

@misc{echo2026,
  title  = {ECHO: Exploratory Compendium of Humanity Online},
  author = {Switzer, Braden and Zafarani, Reza},
  year   = {2026},
  url    = {https://echo.directory},
  note   = {Syracuse University}
}

@misc{https://doi.org/10.48550/arxiv.1603.08861,
  doi       = {10.48550/ARXIV.1603.08861},
  url       = {https://arxiv.org/abs/1603.08861},
  author    = {Yang, Zhilin and Cohen, William W. and Salakhutdinov, Ruslan},
  keywords  = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title     = {Revisiting Semi-Supervised Learning with Graph Embeddings},
  publisher = {arXiv},
  year      = {2016},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

APA

Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory

Yang, Z., Cohen, W. W., & Salakhutdinov, R. (2016). PubMed Network (GCN Benchmark) [Data set]. https://doi.org/10.48550/arXiv.1603.08861

MLA

Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.

Yang, Zhilin, et al. PubMed Network (GCN Benchmark). 2016, doi.org/10.48550/arXiv.1603.08861.

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