# Rank Centrality: Ranking from Pairwise Comparisons

@article{Negahban2017RankCR, title={Rank Centrality: Ranking from Pairwise Comparisons}, author={Sahand Negahban and Sewoong Oh and Devavrat Shah}, journal={Operations Research}, year={2017}, volume={65}, pages={266-287} }

- Published 2017 in Operations Research
DOI:10.1287/opre.2016.1534

The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR’s TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining a ranking, finding ‘scores’ for each object (e.g. player’s rating) is of interest for understanding the intensity of the preferences. In this… CONTINUE READING

#### From This Paper

##### Figures, tables, and topics from this paper.

#### Citations

##### Publications citing this paper.

Showing 1-10 of 48 extracted citations

## Accelerated Spectral Ranking

View 18 Excerpts

Highly Influenced

## Randomized Kaczmarz for rank aggregation from pairwise comparisons

View 5 Excerpts

Highly Influenced

## When can we rank well from comparisons of \(O(n\log(n))\) non-actively chosen pairs?

View 8 Excerpts

Highly Influenced

## Spectral MLE: Top-$K$ Rank Aggregation from Pairwise Comparisons

View 20 Excerpts

Highly Influenced

## Top-K ranking: An information-theoretic perspective

View 5 Excerpts

Highly Influenced

## Adaptive Sampling for Coarse Ranking

View 1 Excerpt

#### Citation Statistics

#### 72 Citations

Citations per Year

Semantic Scholar estimates that this publication has

**72**citations based on the available data.See our **FAQ** for additional information.

#### References

##### Publications referenced by this paper.

Showing 1-10 of 33 references

## equal standard deviations and equal correlations

View 5 Excerpts

Highly Influenced

## Learning Mallows Models with Pairwise Preferences

View 4 Excerpts

Highly Influenced

## Industrial and Applied Mathematics, 564–572

View 4 Excerpts

Highly Influenced

## Information and accuracy attainable in the estimation of statistical parameters

View 12 Excerpts

Highly Influenced

## Selection in the Presence of Noise

View 3 Excerpts

Highly Influenced

## Spectral ranking

View 2 Excerpts

## Rank aggregation via nuclear norm minimization

View 1 Excerpt