Summary of Toward Fair Graph Neural Networks Via Dual-teacher Knowledge Distillation, by Chengyu Li and Debo Cheng and Guixian Zhang and Yi Li and Shichao Zhang
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
by Chengyu Li, Debo Cheng, Guixian Zhang, Yi Li, Shichao Zhang
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 A novel approach to balancing fairness and utility in graph neural networks (GNNs) is proposed, addressing the issue of biased predictions caused by sensitive attributes. The method, FairDTD, uses a dual-teacher distillation framework, incorporating causal graph models and node-specific temperature modules to facilitate information transfer. This strategy leverages full data while optimizing fair representation learning for GNNs, achieving optimal fairness and utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make sure computer programs don’t favor certain groups is introduced. It’s called FairDTD, which uses a special kind of teaching process that helps the program learn to be fair without sacrificing its ability to work well. This approach can help make decisions more objective, which is important for things like hiring or lending. The method was tested on different datasets and showed it could achieve both fairness and good performance. |
Keywords
» Artificial intelligence » Distillation » Representation learning » Temperature
