A Benchmark for Fairness-Aware Graph Learning
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916327862566912 |
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| author | Dong, Yushun Wang, Song Lei, Zhenyu Zheng, Zaiyi Ma, Jing Chen, Chen Li, Jundong |
| author_facet | Dong, Yushun Wang, Song Lei, Zhenyu Zheng, Zaiyi Ma, Jing Chen, Chen Li, Jundong |
| contents | Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware graph learning methods, which blocks practitioners from choosing appropriate ones for broader real-world applications. In this paper, we present an extensive benchmark on ten representative fairness-aware graph learning methods. Specifically, we design a systematic evaluation protocol and conduct experiments on seven real-world datasets to evaluate these methods from multiple perspectives, including group fairness, individual fairness, the balance between different fairness criteria, and computational efficiency. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods. Additionally, we provide practical guidance for applying fairness-aware graph learning methods in applications. To the best of our knowledge, this work serves as an initial step towards comprehensively understanding representative fairness-aware graph learning methods to facilitate future advancements in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Benchmark for Fairness-Aware Graph Learning Dong, Yushun Wang, Song Lei, Zhenyu Zheng, Zaiyi Ma, Jing Chen, Chen Li, Jundong Machine Learning Computers and Society Social and Information Networks Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware graph learning methods, which blocks practitioners from choosing appropriate ones for broader real-world applications. In this paper, we present an extensive benchmark on ten representative fairness-aware graph learning methods. Specifically, we design a systematic evaluation protocol and conduct experiments on seven real-world datasets to evaluate these methods from multiple perspectives, including group fairness, individual fairness, the balance between different fairness criteria, and computational efficiency. Our in-depth analysis reveals key insights into the strengths and limitations of existing methods. Additionally, we provide practical guidance for applying fairness-aware graph learning methods in applications. To the best of our knowledge, this work serves as an initial step towards comprehensively understanding representative fairness-aware graph learning methods to facilitate future advancements in this area. |
| title | A Benchmark for Fairness-Aware Graph Learning |
| topic | Machine Learning Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2407.12112 |