Ghoshal Gourab, Zlatić Vinko, Caldarelli Guido, Newman M E J
Department of Physics and Michigan Center for Theoretical Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Jun;79(6 Pt 2):066118. doi: 10.1103/PhysRevE.79.066118. Epub 2009 Jun 29.
In the last few years we have witnessed the emergence, primarily in online communities, of new types of social networks that require for their representation more complex graph structures than have been employed in the past. One example is the folksonomy, a tripartite structure of users, resources, and tags-labels collaboratively applied by the users to the resources in order to impart meaningful structure on an otherwise undifferentiated database. Here we propose a mathematical model of such tripartite structures that represents them as random hypergraphs. We show that it is possible to calculate many properties of this model exactly in the limit of large network size and we compare the results against observations of a real folksonomy, that of the online photography website Flickr. We show that in some cases the model matches the properties of the observed network well, while in others there are significant differences, which we find to be attributable to the practice of multiple tagging, i.e., the application by a single user of many tags to one resource or one tag to many resources.
在过去几年中,我们目睹了新型社交网络的出现,主要出现在在线社区中,这些社交网络的呈现需要比过去使用的更复杂的图结构。一个例子是大众分类法,它是一种由用户、资源和标签组成的三方结构,用户将标签协作地应用于资源,以便在原本无差别的数据库上赋予有意义的结构。在此,我们提出了这样一种三方结构的数学模型,将其表示为随机超图。我们表明,在大型网络规模的极限情况下,可以精确计算该模型的许多属性,并将结果与真实大众分类法(在线摄影网站Flickr的大众分类法)的观察结果进行比较。我们表明,在某些情况下,该模型与观察到的网络属性匹配良好,而在其他情况下则存在显著差异,我们发现这些差异可归因于多重标记的做法,即单个用户对一个资源应用多个标签或对多个资源应用一个标签。