SherylHYX/GNNRank
Official code for the ICML2022 paper -- GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
This project helps you determine a global ranking from many individual comparisons, like ranking sports teams based on game outcomes or products based on user preferences. It takes in datasets of pairwise comparisons and outputs a comprehensive, ordered list. This is useful for anyone needing to establish a definitive hierarchy from a collection of smaller, relative judgments, such as sports analysts, market researchers, or product managers.
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Use this if you have a dataset where items are compared against each other in pairs (e.g., A beat B, C is preferred over D) and you need to generate a single, overall ranking of all items.
Not ideal if your data is already in a ranked list format or if you need to perform other types of graph analysis beyond global ranking from pairwise comparisons.
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MIT
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Last pushed
Nov 27, 2022
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