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pubmed_linqs — PubMed diabetes papers (LINQS, 2012)

Description

A directed citation network of scientific papers on diabetes from the PubMed database, from the "Pubmed-Diabetes" dataset distributed by the LINQS group (Namata et al.) as a benchmark for collective classification, also used (as one of the "Planetoid" datasets) in many works on graph neural networks. Each of the 19,717 papers is classified into one of three classes, after the Medical Subject Headings (MeSH) of the paper, and described by the TF-IDF weights of the 500 words of a vocabulary of word stems. The data also give, for each paper, the list of the word stems that appear in it (attribute "summary"), which is redundant with the TF-IDF weights and not included. See also the cora_linqs and citeseer_linqs entries.

An edge from paper A to paper B means that A cites B. The data contain 3 self-loops and 11 pairs of papers that cite each other, which are kept.

The network has the following properties:

  • id: the PubMed identifier of the paper.
  • subject: the class of the paper, one of Diabetes Mellitus, Experimental, Diabetes Mellitus Type 1 and Diabetes Mellitus Type 2.
  • words: a vector of 500 values, where the i-th value is the TF-IDF weight of the i-th word of the vocabulary in the paper (0 if the word does not appear in it).
  • Graph property vocabulary: the word stems of the vocabulary, in the order of the values of words.
Tags
Informational Citation Unweighted Metadata Text
Citation
  • G. Namata, B. London, L. Getoor, B. Huang, "Query-driven Active Surveying for Collective Classification." International Workshop on Mining and Learning with Graphs (MLG) (2012) linqs.org
  • P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, T. Eliassi-Rad, "Collective Classification in Network Data." AI Magazine 29(3), 93-106 (2008) 10.1609/aimag.v29i3.2157 Sci-Hub
Upstream URL OK
https://linqs.org/datasets/#pubmed-diabetes
https://web.archive.org/web/20260307191008/https://linqs-data.soe.ucsc.edu/public/
Networks
Tip: hover over a column header for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
pubmed_linqs 19,717 44,338 2.25 7.44 21.65 70.60 -0.04 0.05 17 1.00 Directed Unipartite id subject words 5.0 MiB 5.9 MiB 5.8 MiB 8.1 MiB
Ridiculograms*
pubmed_linqs drawing
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.