Papers › Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
Jiaqing Xie, Rex Ying
Structural features are important features in a geometrical graph. Although there are some correlation analysis of features based on covariance, there is no relevant research on structural feature correlation analysis with graph neural networks. In this paper, we introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network. The results show that there exists high correlation between some of the structural features. An irredundant feature combination with initial node features, which is filtered by graph neural network has improved its classification accuracy in some graph-based tasks. We compare differences between concatenation methods on connecting embeddings between features and show that the simplest is the best. We generalize on the synthetic geometric graphs and certify the results on prediction difficulty between structural features.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | ENZYMES | Fea2Fea-s2 | Accuracy | 48.5 | #42 of 54 | Archive leaderboard | report |
| Graph Classification | NCI1 | Fea2Fea-s3 | Accuracy | 74.9% | #52 of 69 | Archive leaderboard | report |
| Graph Classification | PROTEINS | Fea2Fea-s2 | Accuracy | 77.8% | #25 of 103 | Archive leaderboard | report |
| Graph Classification | Pubmed | Fea2Fea-s3 | Test Accuracy | 78.5 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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