Fairness-Aware Network Embeddings: Methods, Applications, and Challenges

• URL: http://arxiv.org/abs/2608.19381v1
• Date: Wed, 19 Aug 2026 18:51:55 GMT
• Title: Fairness-Aware Network Embeddings: Methods, Applications, and Challenges
• Abstract summary: Real-world networks often reflect structural inequalities from demographic imbalances, homophily, and other societal biases.<n>This survey presents a comprehensive overview of fairness-aware network embeddings for complex networks.
• Score: 16.601913689265547
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 methods along three main complementary dimensions: underlying embedding approach (spectral, random walk, graph neural network, Bayesian, and method-agnostic), fairness intervention strategy (pre-processing, in-processing, and post-processing), and fairness objective criterion (embedding- or task-level). We further compare methods with respect to group versus individual fairness and assumptions regarding sensitive attributes. Finally, we discuss current limitations and highlight promising future research directions. This survey provides a unified perspective on fairness-aware network embedding and serves as a reference for developing fair and trustworthy network representation learning methods.

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