超越同性恋:通过图形的重新连接图形进行类不平衡图形分类
Hui Shen1, Huifang Ma1, Jiyuan Sun1
1College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China.
概括
图形神经网络 (GNN) 在不平衡的数据中扎. GraphBHR通过整合异构和重新连接策略来增强GNN,改善少数群体图形分类.
科学领域:
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
- 不平衡的学习学习
背景情况:
- 图形神经网络 (GNN) 在图形编码方面表现出色,但依赖于平衡的类分布.
- 现实世界的数据往往表现出不平衡的类分布,导致不理想的GNN性能.
- 现有的Graph of Graphs (GoG) 方法假设同型性,限制了它们对少数阶级的有效性.
研究的目的:
- 为了应对使用GNN的不平衡图形分类的挑战.
- 提出一个新的框架,GraphBHR,以改善少数群体图形的表示学习.
- 在不平衡的数据集中增强图谱间监控信号的可靠性.
主要方法:
- 引入了GraphBHR (超越同源性重新连接图形的图形) 框架.
- 综合的异性恋观点,以补充政府对少数群体阶级监督的意见.
- 在GoG传播之前使用图形重新连接策略来优化图形间的关系.
- 使用一致性对比损失和焦点损失来优化表示.
主要成果:
- GraphBHR在处理不平衡的图形分类任务方面表现出显著的有效性.
- 该框架成功地为少数阶级提供了可靠的监管信号.
- 在多尺度数据集上的实验验验证了拟议方法的性能.
结论:
- GraphBHR提供了一个强大的解决方案,用于不平衡的图形分类,通过解决基于同型的方法的局限性.
- 异构和图形重新连接的整合提高了GNN在不平衡数据上的性能.
- 拟议的框架推进了用于现实世界的应用的图形表示学习领域.
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