Francesco Paolo Nerini
Equitable Data Evaluation in Graph Machine Learning.
Rel. Luca Dall'Asta, Paolo Bajardi, Andre' Panisson. Politecnico di Torino, Corso di laurea magistrale in Physics Of Complex Systems (Fisica Dei Sistemi Complessi), 2022
Abstract
Data are becoming every day a more and more central economic asset for companies and public institutions. For this reason, there is an increasing need to provide an equitable evaluation of data. Therefore, having the ability of quantifying the value of data is of paramount importance to take decisions about potential data sharing policies among multiple parties. This is a particularly important question to answer in the case the different parties are competitors, and especially if the nature of the data is private and their transfer is therefore strongly regulated. A solution to the problem of an equitable evaluation is given by the Shapley value, a concept solution from the field of the Cooperative Game Theory.
In this work, we apply this framework in the context of Graph Machine Learning
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