论文标题
在同胞网络中，调整排名具有一般偏见的基于偏见的扩散
Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs
论文作者
论文摘要
同时存在网络可以通过超袋子图（简称HB图）进行充分建模。 HB-graph是一个具有相同宇宙的多组家族，称为顶点集。先前已经提出了一种有效的基于交换的扩散方案，该方案允许对顶点和HB边缘进行排名。在本文中，我们扩展了此方案，以允许不同种类的偏见，并探索它们对获得的不同排名的影响。这些偏见增强了对网络某些特定方面的强调。
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.
