Cora / CiteSeer / PubMed (Planetoid)

data category: Graph learning benchmarks · 0 papers in the last 7 days

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Citation, molecule and other graphs used to compare graph neural networks.

Names searched for: Cora dataset, Cora citation, Planetoid, CiteSeer dataset, Citeseer citation, Cora, CiteSeer

Tasks, access and usage conditions#

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

Dataset comparison: tasks, access and usage conditions
Dataset / version	Task and evaluation	Access and preparation	Usage conditions
Cora / CiteSeer / PubMed — Planetoid splits	Node classification in citation graphs. Evaluation: State transductive or inductive access, features and splits. Test-node features/edges may be visible in transductive learning, but test labels must not train the model.	Official source / documentation Use the authors’ processed files and label masks; identify the dataset and public/fixed versus random split.	The repository’s software license does not automatically relicense every underlying citation/text source; retain original dataset notices.

Guide updated 2026-11-05.

Key findings#

From Nov 2023 to Oct 2026, 105 archived papers mentioned Cora / CiteSeer / PubMed (Planetoid) in their title, abstract, keywords or full text, most of them in Jan 2025 (6).
No paper of the tracked journals mentioned Cora / CiteSeer / PubMed (Planetoid) in the last 30 days (2026-10-07 to 2026-11-05).
From 2017 to 2026 (so far), Cora / CiteSeer / PubMed (Planetoid) was mentioned in 113 papers in the tracked journals, most of them in AI (104), information systems (6) and operations research (2).
41 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were Shanghai Jiao Tong University (5), Griffith University (4) and Hong Kong Polytechnic University (4).
The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (16), IEEE Transactions on Knowledge and Data Engineering (15) and IEEE Transactions on Neural Networks and Learning Systems (12).
The most-cited of them is “Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure” (IEEE Transactions on Knowledge and Data Engineering, 2019), with 234 citations.
Papers use Cora / CiteSeer / PubMed (Planetoid) most often with Open Graph Benchmark (25), Wikipedia (17) and Reddit (16); it has 14 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts, keywords and full texts)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	37	41	27	105

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2018	2019	2022	2023	2024	2025	2026 (so far)
AI	1	2	1	8	34	37	21
Information Systems	0	0	0	0	2	4	0
Operations Research	0	0	0	1	0	0	1
Higher Education and Research	1	0	0	0	0	0	0
All categories	2	2	1	9	36	41	22

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	IEEE Transactions on Pattern Analysis and Machine Intelligence	16
2	IEEE Transactions on Knowledge and Data Engineering	15
3	IEEE Transactions on Neural Networks and Learning Systems	12
4	Knowledge and Information Systems	8
5	Neural Networks	8
6	Machine Learning	7
7	Neural Computing and Applications	5
8	Knowledge-Based Systems	5
9	Information Sciences	4
10	Expert Systems with Applications	4

Institutions with the most papers, since 2023
#	Institution	Papers
1	Shanghai Jiao Tong University	5
2	Griffith University	4
3	Hong Kong Polytechnic University	4
4	Macquarie University	4
5	National University of Defense Technology	4
6	University of Chinese Academy of Sciences	4
7	University of Electronic Science and Technology of China	4
8	Amazon (United States)	3
9	Centre National de la Recherche Scientifique	3
10	Chinese Academy of Sciences	3

Countries with the most papers, since 2023
#	Country	Papers
1	China	58
2	United States	19
3	Australia	18
4	Hong Kong	10
5	India	8
6	Italy	8
7	United Kingdom	7
8	South Korea	5
9	Singapore	5
10	Iran	4

Keywords with the most papers, since 2023
#	Keyword	Papers
1	graph neural networks	56
2	node classification	27
3	graph convolutional networks	11
4	over-smoothing	11
5	node embeddings	10
6	link prediction	9
7	message passing	9
8	contrastive learning	8
9	graph representation learning	8
10	large-scale graphs	7

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	234	2.8	Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure	IEEE Transactions on Knowledge and Data Engineering	2019-12-05	Fuli Feng, Xiangnan He, Jie Tang et al.
2	84	1.9	Echo state graph neural networks with analogue random resistive memory arrays	Nature Machine Intelligence	2023-02-13	Shaocong Wang, Yi Li, Dingchen Wang et al.
3	70	0.7	A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation	IEEE Transactions on Neural Networks and Learning Systems	2018-04-13	Xiaoyan Cai, Junwei Han, Wenjie Li et al.
4	41	1.6	A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications	IEEE Transactions on Knowledge and Data Engineering	2024-09-24	Yi Zhang, Yuying Zhao, Zhaoqing Li et al.
5	38	1.3	Addressing imbalance in graph datasets: Introducing GATE-GNN with graph ensemble weight attention and transfer learning for enhanced node classification	Expert Systems with Applications	2024-06-28	Abdul Joseph Fofanah, David Chen, Lian Wen et al.

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Used together with#

Papers use Cora / CiteSeer / PubMed (Planetoid) most often with Open Graph Benchmark (25), Wikipedia (17) and Reddit (16); it has 14 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok. The whole network →

What papers use Cora / CiteSeer / PubMed (Planetoid) together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
Open Graph Benchmark	Dataset	25	115.0
Wikipedia	Dataset	17	7.1
Reddit	Dataset	16	11.2
Facebook	Dataset	13	1.8
Molecule benchmarks (QM9, ZINC, MUTAG)	Dataset	9	35.3
Amazon reviews	Dataset	8	14.2
ImageNet	Dataset	8	1.4
Weibo	Dataset	5	4.2
IMDb	Dataset	4	7.2
Location and social recommendation data (Gowalla, Foursquare, Yelp…	Dataset	4	8.1
Twitter / X	Dataset	4	0.4
Yelp	Dataset	4	4.6

Cite this page

Scholar4AI. (2026). Cora / CiteSeer / PubMed (Planetoid). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/cora-citeseer-pubmed-planetoid

@misc{scholar4ai-datasets-cora-citeseer-pubmed-planetoid,
  author = {{Scholar4AI}},
  title = {Cora / CiteSeer / PubMed (Planetoid)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/cora-citeseer-pubmed-planetoid}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).