On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections

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[Figure 1, Source: https://www.nature.com/articles/s41598-019-57304-y]


This blog post discusses the ICLR 2021 paper “On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections” by Li et al., highlighting the importance of its theoretical results while critiquing the notions and applications of dyadic fairness presented. This blog post assumes basic familiarity with graph representation learning using message-passing GNNs and fairness based on observed characteristics.


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