超袋图上基于广义有偏交换扩散的同现网络排序调整
Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs
摘要：
共生网络可以用超袋图（简称hb图）来描述。一个hb-图是一族具有相同论域的多重集，称为点集。先前已经提出了一种有效的基于交换的扩散方案，该方案允许顶点和HB-边的排名。在这篇文章中，我们扩展了这个计划，允许不同种类的偏见，并探讨他们对不同的排名获得的效果。这些偏见加强了对网络某些特定方面的强调。
英文摘要：
Co-occurence networks can be adequately modeled by hyper-bag-graphs (hb-graphs for short). A hb-graph is a family of multisets having same universe, called the vertex set. An efficient exchange-based diffusion scheme has been previously proposed that allows the ranking of both vertices and hb-edges. In this article, we extend this scheme to allow biases of different kinds and explore their effect on the different rankings obtained. The biases enhance the emphasize on some particular aspects of the network.
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。
