Addressing Time Bias in Bipartite Graph Ranking for Important Node Identification

TL;DR

This paper introduces a new ranking method for bipartite networks that reduces time bias, ensuring newer high-quality nodes are fairly ranked alongside older ones, improving relevance in real-world applications.

Contribution

The study proposes a rebalance approach to mitigate time bias in bipartite graph ranking algorithms, enhancing the fairness of node quality assessment.

Findings

The method effectively reduces time bias in bipartite ranking.

It improves the ranking accuracy for newer high-quality nodes.

The approach is applicable to various real-world bipartite networks.

Abstract

The goal of the ranking problem in networks is to rank nodes from best to worst, according to a chosen criterion. In this work, we focus on ranking the nodes according to their quality. The problem of ranking the nodes in bipartite networks is valuable for many real-world applications. For instance, high-quality products can be promoted on an online shop or highly reputed restaurants attract more people on venues review platforms. However, many classical ranking algorithms share a common drawback: they tend to rank older movies higher than newer movies, though some newer movies may have a high quality. This time bias originates from the fact that older nodes in a network tend to have more connections than newer ones. In the study, we develop a ranking method using a rebalance approach to diminish the time bias of the rankings in bipartite graphs.