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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.

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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.

关键词:
社区检测 社区检测梯度调制是一种梯度调制.多个行业的多部门.社区信息 社区信息

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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).