In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, in social network analysis (SNA), designing fairness-aware methods for various research problems by considering structural bias and inequalities of large-scale social networks has not received much attention. In this work, we highlight how the structural bias of social networks impacts the fairness of different SNA methods. We further discuss fairness aspects that should be considered while proposing network structure-based solutions for different SNA problems, such as link prediction, influence maximization, centrality ranking, and community detection. This survey-cum-vision clearly highlights that very few works have considered fairness and bias while proposing solutions; even these works are mainly focused on some research topics, such as link prediction, influence maximization, and PageRank. However, fairness has not yet been addressed for other research topics, such as influence blocking and community detection. We review state-of-the-art for different research topics in SNA, including the considered fairness constraints, their limitations, and our vision. This survey also covers evaluation metrics, available datasets, and synthetic network generating models used in such studies. Finally, we highlight various open research directions that require researchers’ attention to bridge the gap between fairness and SNA.

近年来，设计公平感知方法在机器学习、自然语言处理和信息检索等各个领域受到了广泛关注。然而，在社交网络分析（SNA）中，通过考虑大规模社交网络的结构偏差和不平等来设计针对各种研究问题的公平意识方法并没有受到太多关注。在这项工作中，我们强调了社交网络的结构性偏见如何影响不同 SNA 方法的公平性。我们进一步讨论了在针对不同的 SNA 问题提出基于网络结构的解决方案时应考虑的公平性方面，例如链接预测、影响力最大化、中心性排名和社区检测。这项调查和愿景清楚地表明，很少有作品在提出解决方案时考虑到公平性和偏见；即使这些工作主要集中在一些研究主题上，例如链接预测、影响力最大化和PageRank。然而，其他研究主题的公平性尚未得到解决，例如影响力封锁和社区检测。我们回顾了 SNA 中不同研究主题的最新技术，包括考虑的公平性约束、它们的局限性和我们的愿景。这项调查还涵盖了此类研究中使用的评估指标、可用数据集和合成网络生成模型。最后，我们强调了需要研究人员注意弥合公平与国民账户体系之间差距的各种开放研究方向。