NodeImport: Imbalanced Node Classification with Node Importance Assessment
In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance. Traditional GNNs often struggle in such scenarios, as they tend to overfit to majority classes while underrepresenting minority classes. Existing solutions, which either prioritize nodes based on class size or synthesize new nodes for minority classes, often fall short of effectively addressing this imbalance issue. This paper introduces an approach to class-imbalanced node classification by utilizing a balanced
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- LinkedLinked via arxiv author · 85%Nan Chen →
“NodeImport: Imbalanced Node Classification with Node Importance Assessment”
- LinkedLinked via arxiv author · 85%Zemin Liu →
“NodeImport: Imbalanced Node Classification with Node Importance Assessment”
- LinkedLinked via arxiv author · 85%Bryan Hooi →
“NodeImport: Imbalanced Node Classification with Node Importance Assessment”
- LinkedLinked via arxiv author · 85%Bingsheng He →
“NodeImport: Imbalanced Node Classification with Node Importance Assessment”
- LinkedLinked via arxiv author · 85%Jun Huang →
“NodeImport: Imbalanced Node Classification with Node Importance Assessment”
- LinkedLinked via arxiv author · 85%Jia Chen →
“NodeImport: Imbalanced Node Classification with Node Importance Assessment”