社区-CL:基于对比学习的增强社区检测算法
Zhaoci Huang1, Wenzhe Xu1, Xinjian Zhuo1
1School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究介绍了社区对比学习 (Community-CL),这是图形表示学习和社区检测的新框架. 社区-CL通过改进节点嵌入和社区结构发现来增强网络分析.
科学领域:
- 图形表示学习学习学习图形表示.
- 网络分析 网络分析
- 社区检测检测发现
背景情况:
- 图形对比学习 (GCL) 是一种强大的自我监督技术,用于图形分析任务,如节点分类和集群.
- 现有的GCL方法还没有完全探索复杂网络中固有的社区结构.
- 有需要的方法,共同学习节点表示,并有效地检测社区.
研究的目的:
- 提出一个新的在线框架,社区对比学习 (Community-CL),用于同时学习节点表示和社区检测.
- 通过结合社区结构信息来增强图形表示学习.
- 与传统方法相比,提高网络嵌入的准确性和表现力.
主要方法:
- 社区-CL使用对比学习来对准不同图形视图中节点和社区的潜在表示.
- 可学习的图形增强视图是使用图形自动编码器 (GAE) 生成的.
- 一个共享编码器从原始图形及其增强视图中学习节点特征.
主要成果:
- 与最先进的基线相比,社区-CL在社区检测方面取得了更好的表现.
- 该框架在识别网络社区方面表现出更高的准确性.
- 实验结果显示了显著的性能增长,NMI分数为0.714 (亚马逊-照片) 和0.551 (亚马逊-计算机),代表了高达16%的改进.
结论:
- 拟议的社区-CL框架有效地整合了代表性学习和社区检测.
- 联合对比方法导致更准确的网络表示和表达式嵌入.
- 社区-CL为分析网络的社区结构提供了有希望的进展.
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