Survey details fairness-aware network embedding methods

This survey paper provides a comprehensive overview of fairness-aware network embedding methods, which aim to mitigate bias in graph-structured data representations. It categorizes existing approaches based on their underlying embedding technique (e.g., spectral, random walk, graph neural network, Bayesian), fairness intervention strategy (pre-processing, in-processing, post-processing), and fairness objective criterion. The paper also compares methods regarding group versus individual fairness and discusses future research directions for developing trustworthy network representation learning. AI

IMPACT Provides a unified perspective on fair representation learning for complex networks, guiding future research in trustworthy AI.

RANK_REASON The item is a survey paper published on arXiv detailing methods and challenges in a specific AI research area. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• Bayes' theorem
• CatalyzeX
• DagsHub
• Fairness-Aware Network Embeddings
• graph neural network
• Hugging Face
• network embedding
• random walk

• paper
• safety

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