Graph-based rank aggregation method for high-dimensional and partial rankings

Abstract

Rank aggregation has recently become a common approach for combining individual rankings into a consensus and for quantifying and improving performance in various applications, such as elections, web page rankings, and sports. During the past few years, rankings from many sources have become increasingly high-dimensional and partial. In this study, we develop a rank aggregation method by constructing a directed weighted competition graph. We introduce the concept of “ratio of out- and in-degrees (ROID)” to transform high-dimensional partial rankings into a single consensus. Moreover, we provide a novel effectiveness measure for the aggregate ranking according to its deviations from the ground truth ranking. The proposed method is compared with four typical methods with synthetic rankings. The results indicate that our method outperforms the other four by a significant margin and can be particularly efficient in aggregating high-dimensional rankings. The empirical results validate the effectiveness and feasibility of our method.

Keywords:

• Decision analysis
• group ranking
• rank aggregation
• competition graph
• ratio of out- and in-degrees (ROID)

Acknowledgements

We thank Yapeng Li, Mingze Qi and Ye Deng for their helpful insights.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Jun Wu acknowledges the National Natural Science Foundation of China under Grant Nos. 71871217, 71690233, 71371185 and the Natural Science Foundation of Hunan Province under Grant No. 2019JJ20019. Hongzhong Deng acknowledges the National Natural Science Foundation of China under Grant No. 71771214. Xin Lu acknowledges the National Natural Science Foundation of China under Grant No. 71522014, 71771213, 71790615, and 91846301.