Fairness-Aware Network Embeddings: Methods, Applications, and Challenges
Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization. However, real-world networks often reflect structural inequalities arising from demographic imbalances, homophily, and other societal biases, which fairness-agnostic embedding methods can encode and amplify. To address this issue, numerous fairness-aware network embedding methods have been proposed to mitigate bias while preserving embedding utility. This survey presents a comprehensive overview of fairness-aware network embeddings for complex networks. We propose a taxonomy that categorizes existing ...
| Authors | Ella Has, Harshith Kumar Yadav, Gaurav Dixit et al. |
|---|---|
| Primary category | cs.SI |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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