arXiv Artificial Intelligence

GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning

1 hour ago 16.09.2026 07:00 👁 2 views
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning

Quick summary

arXiv:2509.23616v2 Announce Type: replace-cross Abstract: The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepresented. This issue is also prevalent in graph-structured data. Most graph neural networks (GNNs) implicitly assume a balanced class distribution and therefore often fail to account for the challenges introduced by class imbalance, which can lead to biased learning and degraded performance on minority classes. We identify a quality inconsistency problem in syn

Key takeaways

  • arXiv:2509.23616v2 Announce Type: replace-cross Abstract: The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepresented.
  • This issue is also prevalent in graph-structured data.
  • Most graph neural networks (GNNs) implicitly assume a balanced class distribution and therefore often fail to account for the challenges introduced by class imbalance, which can lead to biased learning and degraded performance on minority classes.

Why it matters

“GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗