深度学习代表性和多层网络集群的歧视性特征
Wenming Wu1, Xiaoke Ma1, Quan Wang1
1School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, 710071, China.
概括
一个新的图形集群算法DRDF有效地识别多层网络中的特定层模块. 它平衡了模块连接性和特异性,在准确性和稳定性方面超过了现有的方法.
科学领域:
- 网络科学 网络科学
- 图形理论 图形理论
- 数据挖掘 数据挖掘
背景情况:
- 多层网络模拟具有相互作用层的复杂系统.
- 特定层的模块对于理解网络结构和功能至关重要.
- 由于复杂的层合,现有的方法难以平衡模块连接性和特异性.
研究的目的:
- 提出一种新的联合学习图集群算法 (DRDF),用于检测多层网络中的特定层模块.
- 同时学习深度表示和区分特征,以准确检测模块.
- 为了有效地平衡层特定模块的连接性和特异性.
主要方法:
- DRDF使用深度非负矩阵分解来学习深度表示,并描述高阶网络拓.
- 具有自我监督信息的歧视性特征学习 (类内紧性和类间分离) 增强了模块的特异性.
- 联合学习框架通过衍生目标函数和优化规则平衡连接性和特异性.
主要成果:
- DRDF准确地描述了多层网络的高阶拓.
- 该算法通过探索伪标签作为自我监督的信息来明确模拟模块特异性.
- 在十个多层网络上的实验表明DRDF在图形集群和稳定性方面比八种基准方法的性能优越.
结论:
- DRDF提供了一种更准确的方法来明确建模多层网络的特异性.
- 拟议的算法有效平衡了模块连接性和特异性.
- DRDF增强了多层网络图形集群算法的稳定性.
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