Summary of Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations, by Thijmen Nijdam et al.
Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
by Thijmen Nijdam, Juell Sprott, Taiki Papandreou-Lazos, Jurgen de Heus
First submitted to arxiv on: 31 Aug 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 study conducts a reproducibility analysis of the 2022 paper “Learning Fair Graph Representations Via Automated Data Augmentations” by Ling et al. The authors assess the validity of the original claims focused on node classification tasks and explore the performance of the Graphair framework in link prediction tasks. The investigation reveals that one of the original three claims can be partially reproduced, while the other two are fully substantiated. Additionally, the study broadens the application of Graphair from node classification to link prediction across various datasets. The findings indicate that Graphair demonstrates a comparable fairness-accuracy trade-off to baseline models for mixed dyadic-level fairness, but has a superior trade-off for subgroup dyadic-level fairness. These findings underscore Graphair’s potential for wider adoption in graph-based learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers checked if the results from a 2022 paper about using computers to learn fair representations of graphs were correct. They tested how well the computer program, called Graphair, worked on different types of tasks and with different data. The scientists found that some parts of the original paper were right, but others needed more testing. They also showed that Graphair can be used for new kinds of graph-based learning. This is important because it means Graphair could be used in many different areas where graphs are used. |
Keywords
» Artificial intelligence » Classification
