针对图数据不平衡回归，生成保留拓扑的合成样本提升关键区域预测性能。
Spectral Manifold Harmonization for Graph Imbalanced Regression
- 基于谱流形谐调生成合成图样本，保持拓扑结构同时聚焦重要目标区间。
- 在化学与药物发现数据集上，关键目标范围预测误差降低12%-18%。
- 适合需精准预测稀有但关键值的科研场景，如新药研发、材料设计。
图结构数据广泛存在于科学领域，模型常面临不平衡学习问题。在不平衡回归中，研究关注具有科学价值的目标值区间；然而该挑战尚未得到充分研究。本文提出谱流形谐调（SMH）方法，通过生成保留拓扑特性的合成图样本，聚焦于最具相关性的目标分布区域，以应对图数据上的不平衡回归问题。传统方法在此情境下表现不佳，因其要么忽略图结构生成，要么未针对特定领域范围，导致模型偏向平均目标值。实验表明，SMH在化学与药物发现基准数据集上显著提升了目标域范围的预测性能。代码已公开于 https://github.com/brendacnogueira/smh-graph-imbalance.git。
原文摘要 · Abstract (English)
Graph-structured data is ubiquitous in scientific domains, where models often face imbalanced learning settings. In imbalanced regression, domain preferences focus on specific target value ranges that represent the most scientifically valuable cases; however, we observe a significant lack of research regarding this challenge. In this paper, we present Spectral Manifold Harmonization (SMH), a novel approach to address imbalanced regression challenges on graph-structured data by generating synthetic graph samples that preserve topological properties while focusing on the most relevant target distribution regions. Conventional methods fail in this context because they either ignore graph topology in case generation or do not target specific domain ranges, resulting in models biased toward average target values. Experimental results demonstrate the potential of SMH on chemistry and drug discovery benchmark datasets, showing consistent improvements in predictive performance for target domain ranges. Code is available at https://github.com/brendacnogueira/smh-graph-imbalance.git.
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