使用 egonet 特性分布进行网络比较的指标
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy. carlo.piccardi@polimi.it.
Scientific reports
|September 5, 2023
概括
我们开发了一种新的网络比较方法,使用本地节点特征创建全球网络肖像. 这种方法有效量化了网络不相似性,性能与现有的最先进技术相提并论.
科学领域:
- 网络科学 网络科学
- 图形理论 图形理论
- 数据分析 数据分析
背景情况:
- 量化网络不相似性对于识别类似网络集团或检测时间网络序列的变化等任务至关重要.
- 现有的方法通常需要网络对齐或具有高计算成本.
研究的目的:
- 提出一种新的,无对齐的方法来量化网络不相似性.
- 为了利用本地 egonet 特性进行全球网络比较方法.
主要方法:
- 全球网络肖像是通过处理本地节点特征,特别是程度,集群系数和 egonet 持久性来构建的.
- 不相似度指标来自于这些 egonet 特性在整个网络中的分布.
- 分布函数与计算网络距离进行比较,而不需要网络对齐.
主要成果:
- 拟议的方法在分类测试中表现出有效性,准确地识别来自同一合成模型的图形.
- 性能与最新的基于图表的方法相美.
- 计算要求与现有的先进技术相似.
结论:
- 拟议的方法为网络比较提供了一个简单,灵活和有效的方法.
- 它为当前的网络不相似性量化技术提供了可行的替代方案.
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