Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs

TL;DR

This paper extends an exchange-based diffusion scheme for hyper-bag-graphs to include biases, enabling more tailored ranking of vertices and edges by emphasizing specific network features.

Contribution

It introduces a biased diffusion method for hyper-bag-graphs, allowing customizable emphasis in ranking results, which was not previously available.

Findings

Biases influence the ranking outcomes significantly.

The method enhances the flexibility of diffusion-based ranking.

Different bias configurations highlight various network aspects.

Abstract

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.