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无规模小世界图的高阶变换检验

Examining Higher Order Transformations for Scale-free Small World Graphs
课程网址: http://videolectures.net/eccs07_quasthoff_eho/  
主讲教师: Uwe Quasthoff
开课单位: 莱比锡大学
开课时间: 2007-12-14
课程语种: 英语
中文简介:
无规模小世界网络的程度分布遵循幂律。对于随机图生成器,其指数受构造机制的约束,而在现实世界数据中,可以观察到不同的斜率。然而,单独的度分布并未揭示这些图的局部结构。因此,我们提出了一种图形变换,我们称之为“高阶”变换,它对两个顶点共享边缘权重的共同邻居的数量进行编码。研究二阶和三阶图的度分布并将其与自然语言共生数据进行比较,我们发现高阶变换揭示了仅通过查看原始图上的传统度量无法检测到的差异。
课程简介: The degree distribution of scale-free Small World networks follows a power law. For random graph generators, its exponent is constrained by the construction mechanism, whereas in real-world data, different slopes can be observed. However, the degree distribution alone does not reveal much of the local structure of these graphs. Therefore, we propose a graph transformation we call ”higher order” transformation, which encodes the number of common neighbours two vertices share in its edge weights. Studying the degree distribution of secondand third order graphs and comparing it to natural language cooccurrence data, we find that the higher order transformation reveals differences that cannot be detected by only looking at traditional measures on the original graph.
关 键 词: 幂律; 随机图生成器; “高阶”变换
课程来源: 视频讲座网
最后编审: 2019-03-19:lxf
阅读次数: 35