天空地图:GNN基准测试的生成图形模型
Axel Wassington1, Raúl Higueras1, Sergi Abadal1
1Department of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona, Spain.
Frontiers in artificial intelligence
|November 29, 2024
概括
SkyMap生成了合成标记的属性图形,改善了图形神经网络 (GNN) 性能复制. 这种新模型提供了对图形拓和特征的精细控制,优于GNN基准测试的现有生成方法.
科学领域:
- 机器学习 机器学习
- 图形理论 图形理论
- 数据科学数据科学数据科学
背景情况:
- 图形神经网络 (GNN) 越来越受欢迎,但研究受到一小部分基准数据集的限制.
- 现有的合成图形生成模型 (例如,ALBTER,GenCAT) 通常无法准确地反映GNN在原始数据上的性能.
研究的目的:
- 介绍SkyMap,一个用于标记属性图的新型生成模型.
- 为合成数据生成提供对图形拓和特征分布的细粒度控制.
- 通过使用更多样化和更具代表性的合成数据集,加强GNN的评估和基准分析.
主要方法:
- 开发了SkyMap,这是一个具有可控制拓和特征分布的标记属性图的生成模型.
- 评估了SkyMap在各种GNN架构 (图形卷积,注意力,等态网络) 中复制图形可学习性的能力.
- 使用瓦瑟斯坦距离进行量化性能复制,并通过参数抽样证明了通过参数抽样生成数据集星座.
主要成果:
- 与ALBTER和GenCAT相比,SkyMap在复制GNN可学习性方面表现出卓越的表现,实现了64%更低的瓦斯斯坦距离.
- 该模型可以通过采样输入参数来创建各种合成图数据集.
- 在GNN和多层感知子之间的性能比较中,SkyMap生成的数据集的实用性得到了说明.
结论:
- 天空地图为GNN评估生成高保真度合成图形数据集提供了重大进步.
- 该模型的细粒度控制和生成多种数据集的能力解决了当前GNN基准分析实践的局限性.
- 通过提供量身定制的合成数据,SkyMap促进了更强大,更可靠的GNN研究.
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