Summary of Unveiling the Impact Of Local Homophily on Gnn Fairness: In-depth Analysis and New Benchmarks, by Donald Loveland et al.
Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks
by Donald Loveland, Danai Koutra
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates Graph Neural Networks’ (GNNs) limitations when dealing with graphs featuring both homophily (same-class connections) and heterophily (different-class connections). Specifically, it examines how local homophily levels can affect GNNs’ performance and fairness. The authors formalize the challenge of fair predictions for underrepresented homophily levels as an out-of-distribution (OOD) problem and conduct a theoretical analysis demonstrating how local homophily levels alter predictions for different sensitive attributes. They also introduce three new GNN fairness benchmarks and a semi-synthetic graph generator to empirically study this OOD problem. The results show that two factors can promote unfairness: the distance from in-distribution data (OOD distance) and heterophilous nodes situated in homophilous graphs. This leads to a drop in fairness by up to 24% on real-world datasets and 30% in semi-synthetic datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computer programs called Graph Neural Networks (GNNs) work when dealing with complex data like social networks or molecular structures. GNNs are good at predicting things, but they can be biased if the data they’re looking at is unfair. The authors of this paper want to understand why GNNs make mistakes and how we can fix them. They found that if the data has some parts that are very different from others, the GNN will make more mistakes and be less fair. This matters because GNNs are used in many important applications like medical diagnosis or predicting what movies you’ll like. |
Keywords
» Artificial intelligence » Gnn
