Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914615204511744 |
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| author | Marey, Lilian Perez, Mathilde Viard, Tiphaine Laclau, Charlotte |
| author_facet | Marey, Lilian Perez, Mathilde Viard, Tiphaine Laclau, Charlotte |
| contents | Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this task. While many fairness-aware methods manipulate graph structures to mitigate prediction disparities, the topological biases inherent to social graphs remain poorly understood and are consistently conflated with homophily alone. In this work, we study the relationship between structural biases and fairness outcomes in LP. To this end, we formalize a taxonomy of topological bias measures and introduce a graph generation method producing a diverse corpus of synthetic graphs with controlled structural properties. Using this corpus, we show empirically that fairness outcomes are strongly correlated with graph topology, and that current fairness-aware methods remain sensitive to structural biases beyond homophily. These findings highlight the need for structurally grounded evaluations in fair graph learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11802 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction Marey, Lilian Perez, Mathilde Viard, Tiphaine Laclau, Charlotte Machine Learning Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this task. While many fairness-aware methods manipulate graph structures to mitigate prediction disparities, the topological biases inherent to social graphs remain poorly understood and are consistently conflated with homophily alone. In this work, we study the relationship between structural biases and fairness outcomes in LP. To this end, we formalize a taxonomy of topological bias measures and introduce a graph generation method producing a diverse corpus of synthetic graphs with controlled structural properties. Using this corpus, we show empirically that fairness outcomes are strongly correlated with graph topology, and that current fairness-aware methods remain sensitive to structural biases beyond homophily. These findings highlight the need for structurally grounded evaluations in fair graph learning. |
| title | Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.11802 |