一个多分支培训战略,以加强社区信号在GNN的社区检测信号
Yuning Guo1, Qiang Wu2, Linyuan Lü3
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
图形神经网络 (GNN) 由于过度平滑而难以检测社区. 本研究引入了一种新的培训策略,以增强当地信号,改善社区在复杂网络中的识别.
科学领域:
- 图形神经网络 图形神经网络
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 在复杂的网络中,社区检测至关重要.
- 图形神经网络 (GNN) 是主流的方法.
- 全球基因网络遭受拉普拉斯式过度平滑,稀释重要的邻里信号.
研究的目的:
- 提出一种新的培训策略,以加强社区检测的当地信号.
- 解决GNN在社区识别任务中的过度平滑造成的矛盾.
主要方法:
- 设计了一个多分支学习结构,在反向传播过程中注入梯度.
- 模块化梯度与GNN的消息传递路径,以补充初始层的第一阶拓信息.
- 构建了基于网络结构的GNN (NIGNN).
主要成果:
- 与基本模型相比,在社区检测任务中实现了0.6-3.6%的改进.
- 在t测试中表现良好.
- 在信息不完整的网络中表现出强大的稳定性.
结论:
- 拟议的方法有效地保留了深度GNN中的核心本地信息.
- NIGNN框架为增强社区检测提供了一个新的解决方案.
- 这种方法广泛适用于各种GNN架构 (GCN,GAT,GraphSAGE).
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