加特费尔帕集成图表注意力网络和增强的标签传播,以实现强大的社区检测
Feiyi Tang1, Junxian Li1, Xi Liu1
1School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China.
Scientific reports
|January 31, 2025
概括
一个新的混合模型GATFELPA通过减轻过度平滑和提高可扩展性来增强图形社区检测. 这种方法为复杂的网络分析提供了卓越的准确性和稳定性.
科学领域:
- 图形神经网络的神经网络
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 社区检测对于理解图形网络至关重要.
- 传统的图形卷积网络 (GCN) 面临过度平滑和可扩展性问题.
- 现有的方法与大规模和异质网络作斗争.
研究的目的:
- 开发一种混合模型,用于有效的社区检测.
- 解决图形网络中的过度平滑和计算效率低下问题.
- 为了提高社区检测的准确性,稳定性和可扩展性.
主要方法:
- 提出了GATFELPA,这是一个混合模型,结合了图形注意网络 (GAT) 和增强的标签传播 (DPCELPA).
- 实施了适应性策略,以优化聚合层,减少过度光滑.
- 引入了一个相似性保护模块,以保留网络不相似性.
主要成果:
- 加特费尔帕在现实世界数据集上表现出卓越的表现,包括ogbn-arxiv.
- 在准确性,稳定性和可扩展性方面取得了显著的改进.
- 有效平衡的社区内部紧性和社区内部可分离性.
结论:
- 对于复杂的社区检测任务,GATFELPA提供了一个有前途的解决方案.
- 该模型有效地减轻了过度平滑,并提高了可扩展性.
- 在大规模网络上,在准确性和稳定性方面优于现有的方法.
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