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

arXiv (Cornell University)

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.

Authors 5

1. Ella Has Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Leiden , The Netherlands

2. Harshith Kumar Yadav
3. Gaurav Dixit

   Indian Institute of Technology Roorkee

   Affiliation as printed

   Mehta Family School of DS and AI IIT Roorkee , India

4. Mykola Pechenizkiy

   Eindhoven University of Technology

   Affiliation as printed

   Eindhoven University of Technology Eindhoven , The Netherlands

5. Akrati Saxena Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Leiden , The Netherlands