通过相邻光谱嵌入和图形编码器嵌入在随机点产品图中检测基于模型的种植伪集团
IEEE transactions on pattern analysis and machine intelligence
|December 12, 2025
概括
邻近光谱嵌入 (ASE) 和图形编码器嵌入 (GEE) 方法在没有清洁数据的随机点产物图中与伪点击检测作斗争. 然而,当有额外的清洁网络数据可用时,他们可以定位这些结构.
科学领域:
- 图形理论是指图形的理论.
- 网络分析 网络分析
- 机器学习 机器学习
背景情况:
- 伪集团是网络中的子结构,可以表明社区形成或异常.
- 随机点积图是一种用于研究网络属性的生成模型.
- 频谱嵌入方法是分析图形结构的常见方法.
研究的目的:
- 评估相邻光谱嵌入 (ASE) 和图形编码器嵌入 (GEE) 在检测嵌入式伪集群的有效性.
- 将这些方法与现有的光谱集团检测技术进行比较.
- 评估ASE和GEE的稳定性,以建模污染和额外的清洁数据的影响.
主要方法:
- 在随机点积图的背景下对ASE和GEE进行理论分析.
- 使用模拟和真实世界的网络数据进行实证评估.
- 包括变量图自动编码器 (VGAE) 模型用于比较分析.
主要成果:
- 当没有提供额外的清洁网络数据时,ASE和GEE的性能比现有的光谱集团方法要差.
- 当清洁,独立的网络数据被引入时,这些方法可以在异常地定位伪集团.
- 该研究强调了这些方法在捕获伪集团方面的可变能力,以及它们在模拟污染方面的强度.
结论:
- ASE和GEE显示了伪集团检测的潜力,但对数据质量和可用性敏感.
- 这些图形嵌入方法的有效性取决于清洁,补充网络数据的存在.
- 进一步的研究可以探索混合方法或模型改进,以提高伪集团检测能力.
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