标签高效规范化和传播用于图形节点分类
IEEE transactions on pattern analysis and machine intelligence
|August 30, 2023
概括
一种新的标签高效规范化和传播 (LERP) 方法通过自适应性地确定可靠的伪标签来改进图节点分类,优于GraphHop等现有方法,特别是具有有限的标签数据.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 图形卷积网络 (GCN) 是半监督节点分类的最新技术.
- 像GraphHop这样的现有方法提供了直观的解释,但缺乏严格的数学处理.
- GraphHop的性能受到其处理伪标签节点和标签聚合的限制.
研究的目的:
- 为图节点分类提出一个标签有效规范化和传播 (LERP) 框架.
- 为了解决GraphHop在处理伪标签节点和标签聚合方面的局限性.
- 为半监督节点分类开发一种高效且理论上有保证的方法.
主要方法:
- 开发了一个新的标签高效规范化和传播 (LERP) 框架.
- 引入了一个用于解决LERP框架的交替优化程序.
- 提出了LERP方法,该方法通过自适应性来确定可靠的伪标签,并完善标签聚合.
主要成果:
- 在各种数据集中,LERP方法始终优于GraphHop和其他基准分析方法.
- 即使在极低的标签率 (每类1-20个样本) 下,LERP也表现出卓越的性能.
- 保证了LERP方法的理论趋同.
结论:
- LERP框架为半监督节点分类提供了一个数学严格的方法.
- LERP有效地解决了GraphHop的缺陷,提供了更好的准确性和效率.
- LERP是一种高效和计算效率的方法,用于图节点的分类,特别是在低标签的制度.
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