GMN-Zoomer:通过层次分析,聚合和匹配来学习图形相似性
Ke-Jia Chen1, Yusheng Chen2, Ziyue Wang2
1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China; Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.
概括
本研究介绍了一种新的图形相似性学习框架,它捕捉了图形结构层次结构. 与现有模型相比,该方法显著减少了错误,提高了图形比较的准确性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 传统的图形相似度指标面临着NP难度的挑战.
- 现有的基于学习的方法忽视了图形结构的等级性质.
- 准确的图形相似性对于各种应用至关重要.
研究的目的:
- 提出一个图形相似性学习框架,利用层次图形结构.
- 为了提高图形相似度测量的准确性和稳定性.
- 为了解决捕获多颗粒度图相似性的当前方法的局限性.
主要方法:
- 通过结构解析和层次聚合生成层次图形.
- 在相同的颗粒度水平上匹配图形,以实现交叉图形交互.
- 仔细合所有细粒度级别的匹配结果,以获得最终的相似度得分.
主要成果:
- 与最先进的模型相比,平均二次平均误差减少了28.65%.
- 在不同层次解析策略中表现出强大的性能.
- 在三个基准数据集上表现优于现有方法.
结论:
- 提出的等级图形相似性学习框架有效地捕捉了在多个细分度上的结构相似性.
- 该方法比目前最先进的方法提供了显著的改进.
- 层次分析,聚合和匹配是增强图形相似性学习的关键.
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