发表机构
LIACS, Leiden University; IIT Ropar; IIT Roorkee; Eindhoven University of Technology（莱顿大学LIACS学院; 印度理工学院罗帕尔分校; 印度理工学院鲁基分校; 埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述梳理面向公平性的网络嵌入方法，提出多维度分类体系，比较相关方法的公平性维度差异，指出当前局限与未来方向，为开发公平可信的网络表示学习方法提供参考。
AI 中文摘要
网络嵌入方法学习图结构数据的低维表示，以支持节点分类、链接预测和影响力最大化等下游任务。然而，现实网络往往反映出由人口统计学失衡、同质性及其他社会偏见引发的结构不平等，不考虑公平性的嵌入方法会将这些偏见编码并放大。为解决该问题，大量面向公平性的网络嵌入方法被提出，以在保留嵌入效用的同时减轻偏见。本综述全面概述了复杂网络的面向公平性的网络嵌入，提出了一种分类体系，从三个互补的主要维度对现有方法进行分类：底层嵌入方法（谱方法、随机游走、图神经网络、贝叶斯方法及与方法无关的方法）、公平干预策略（预处理、处理中、后处理）、公平目标准则（嵌入级或任务级）。我们进一步从群体公平性与个体公平性、敏感属性相关假设等方面对方法进行比较，最后讨论了当前局限性并指出有前景的未来研究方向。本综述为面向公平性的网络嵌入提供了统一视角，可作为开发公平可信的网络表示学习方法的参考。
英文摘要
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.
