k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods
arXiv:2603.03867v1 Announce Type: new Abstract: Link prediction (LP) plays a central role in graph-based applications, particularly in social recommendation. However, real-world graphs often reflect structural biases, most notably homophily, the tendency of nodes with similar attributes to connect. While this property can improve predictive performance, it also risks reinforcing existing social disparities. In response, fairness-aware LP methods have emerged, often seeking to mitigate these effects by promoting inter-group connections, that is, links between nodes with differing sensitive attributes (e.g., gender), following the principle of dyadic fairness. However, dyadic fairness overlooks potential disparities within the sensitive groups themselves. To overcome this issue, we propose $k$-hop fairness, a structural notion of fairness for LP, that assesses disparities conditioned on the distance between nodes in the graph. We formalize this notion through predictive fairness and str
arXiv:2603.03867v1 Announce Type: new Abstract: Link prediction (LP) plays a central role in graph-based applications, particularly in social recommendation. However, real-world graphs often reflect structural biases, most notably homophily, the tendency of nodes with similar attributes to connect. While this property can improve predictive performance, it also risks reinforcing existing social disparities. In response, fairness-aware LP methods have emerged, often seeking to mitigate these effects by promoting inter-group connections, that is, links between nodes with differing sensitive attributes (e.g., gender), following the principle of dyadic fairness. However, dyadic fairness overlooks potential disparities within the sensitive groups themselves. To overcome this issue, we propose $k$-hop fairness, a structural notion of fairness for LP, that assesses disparities conditioned on the distance between nodes in the graph. We formalize this notion through predictive fairness and structural bias metrics, and propose pre- and post-processing mitigation strategies. Experiments across standard LP benchmarks reveal: (1) a strong tendency of models to reproduce structural biases at different $k$-hops; (2) interdependence between structural biases at different hops when rewiring graphs; and (3) that our post-processing method achieves favorable $k$-hop performance-fairness trade-offs compared to existing fair LP baselines.
Executive Summary
The article introduces k-hop fairness, a novel notion of fairness for link prediction in graph-based applications. It addresses disparities beyond first-order neighborhoods by assessing predictive fairness and structural bias metrics. The authors propose pre- and post-processing mitigation strategies and demonstrate their effectiveness through experiments on standard benchmarks, revealing the tendency of models to reproduce structural biases and the interdependence of biases at different hops.
Key Points
- ▸ Introduction of k-hop fairness for link prediction
- ▸ Assessment of disparities conditioned on node distance in the graph
- ▸ Proposal of pre- and post-processing mitigation strategies
Merits
Comprehensive approach
The article provides a thorough analysis of fairness in link prediction, considering multiple aspects and proposing effective mitigation strategies.
Demerits
Limited generalizability
The experiments are conducted on standard benchmarks, which may not fully represent real-world graphs and their complexities.
Expert Commentary
The introduction of k-hop fairness represents a significant advancement in the field of link prediction, as it acknowledges the complexities of real-world graphs and the need to address disparities beyond immediate neighborhoods. The proposed mitigation strategies demonstrate promising results, but further research is necessary to fully understand the implications of k-hop fairness and its potential applications. The article's findings also underscore the importance of considering fairness and transparency in the development of AI systems, particularly those that rely on graph-based models.
Recommendations
- ✓ Future research should investigate the application of k-hop fairness to diverse graph-based applications and explore its potential to promote fairness and reduce biases in real-world systems.
- ✓ Developers of graph-based models should consider incorporating k-hop fairness metrics and mitigation strategies to ensure fairness and transparency in their systems.