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arXiv AI By Fanlong Zeng, Wensheng Gan, Kangjie Chen, Philip S. Yu

GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning

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GraphIFE addresses the class imbalance problem in graph-structured data by tackling a quality inconsistency issue in synthesized nodes. The framework uses graph invariant learning to strengthen embedding space representations and identify invariant features, leading to improved performance on minority classes. Experiments show that GraphIFE consistently outperforms various baselines across multiple datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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