Ordering by weighted number of wins gives a good ranking for weighted tournaments

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

KORE: keyphrase overlap relatedness for entity disambiguation (2012, background): “[7], which has shown that the number of times an entity wins and the weights of the wins gives a good ranking.”
Rank Aggregation: Together We're Strong (2009, methodology): “Several algorithms are known for rank aggregation with performance guarantees of 2 or less, see for example [2], [1], [7], [20], [19].”
Label ranking by learning pairwise preferences (2008, background): “15 For example, it has recently been shown in [15] that optimizing rank correlation yields a 5-approximation to the ranking which is optimal for the Kendall measure.”
The Algorithm Design Manual (2020, background): “Heuristics for ranking tournaments are discussed in [CFR06].”
Handbook of Computational Social Choice (2016, background): “Ordering the alternatives by increasing Borda score provides a 5-approximation to Kemeny Rank Aggregation (Coppersmith et al., 2010).”
Group recommendations with rank aggregation and collaborative filtering (2010, methodology): “However, imagine, that CF rating prediction method made some mistakes and returned the ranked list {1, 8, 4, 9, 7}, with the corresponding ratings: [5, 4, 5, 2, 3].”