标记社交网络的超图拓扑量
Hypergraph topological quantities for tagged social networks
摘要：
近年来，出现了一类新的社交网络，这需要我们超越以前采用的表示复杂的图形结构。一个值得注意的例子是大众分类法（folksonomy），这是一个在线过程，用户协作地使用资源的标签来赋予一个没有区别的数据库结构。在最近的一篇论文[1]中，我们提出了一个数学模型，将这些结构表示为三部超图，并定义了感兴趣的基本拓扑量。在本文中，我们扩展了我们的模型定义额外的数量，如边缘分布，顶点相似性和相关性以及聚类。然后，我们实证测量这些数量的两个现实生活中的大众分类，流行的在线照片共享网站Flickr和书签网站CiteULike。我们发现，这些系统具有类似的定性特征与大多数复杂网络，以前已经研究过。我们建议，这里描述的数量和方法可以作为一个标准的工具，在测量标记网络的结构。
英文摘要：
Recent years have witnessed the emergence of a new class of social networks, that require us to move beyond previously employed representations of complex graph structures. A notable example is that of the folksonomy, an online process where users collaboratively employ tags to resources to impart structure to an otherwise undifferentiated database. In a recent paper[1] we proposed a mathematical model that represents these structures as tripartite hypergraphs and defined basic topological quantities of interest. In this paper we extend our model by defining additional quantities such as edge distributions, vertex similarity and correlations as well as clustering. We then empirically measure these quantities on two real life folksonomies, the popular online photo sharing site Flickr and the bookmarking site CiteULike. We find that these systems share similar qualitative features with the majority of complex networks that have been previously studied. We propose that the quantities and methodology described here can be used as a standard tool in measuring the structure of tagged networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
