Addressing Time Bias in Bipartite Graph Ranking for Important Node Identification
This addresses a specific issue in network ranking for applications like online shops and review platforms, but it is incremental as it builds on existing methods to mitigate a known bias.
The paper tackles the problem of time bias in bipartite graph ranking, where older nodes are favored over newer ones, by developing a rebalance approach to reduce this bias for more accurate node quality ranking.
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.