Self-Supervised Learning of Graph Neural Networks: A Unified Review is a research paper published in IEEE Transactions on Pattern Analysis and Machine Intelligence (2022). On theSindex it has a DataRank of 0.887. It has been cited 369 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.887
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Science Foundation
Grant: IIS-2006861
National Science Foundation
Grant: DBI-2028361
National Institutes of Health
Grant: 1R21NS102828
National Science Foundation
Grant: 2006861
III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications
National Science Foundation
Grant: 2028361
Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets
National Institutes of Health
Grant: 1R21NS102828-01
Deep Learning for Connectomics
FWCI
37.05
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords