In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, understanding structural bias and inequalities in social networks and designing fairness-aware methods for various research problems in social network analysis (SNA) have 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 paper 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 paper 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.

翻译：近年来，设计具有公平性意识的方法在机器学习、自然语言处理和信息检索等多个领域受到广泛关注。然而，理解社交网络中的结构性偏见与不平等，以及为社交网络分析中的各类研究问题设计公平性方法，却尚未引起足够重视。本文重点揭示社交网络的结构性偏见如何影响不同SNA方法的公平性，并进一步探讨在为链接预测、影响力最大化、中心性排序和社区检测等SNA问题提出基于网络结构的解决方案时，应考虑的公平性因素。本文明确指出，极少有研究在提出解决方案时考量公平性与偏见问题；即便这些研究也主要集中于部分主题（如链接预测、影响力最大化和PageRank）。然而，公平性在其他研究主题（如影响力阻断和社区检测）中仍未得到解决。我们综述了SNA不同研究主题的最新技术进展，包括所采用的公平性约束、其局限性及我们的展望。本文还涵盖了此类研究中使用的评估指标、可用数据集及合成网络生成模型。最后，我们强调了一些亟需研究者关注的新兴研究方向，以弥合公平性与SNA之间的差距。