Uncertainty theory, founded in 2007, has become a branch of mathematics to model uncertainty rather than randomness. As an indispensable part of uncertainty theory, uncertain graph and uncertain network optimization has received the wide attention of many scholars. Naturally, a series of original research achievements have been obtained on uncertain graph and uncertain network optimization. This paper aims to present a state-of-the-art review on the recent advance in uncertain graph and uncertain network optimization. Furthermore, it hopes to predict the possible future research directions. Based on Web of Science database, this paper retrieves 144 related papers from 2011 to 2021 to analyze the features of published articles. More precisely, we analyze the annual number of publications, key topics and sub-fields, journals, and most-cited articles. In addition, the main results and models for uncertain graph and uncertain network optimization are summarized. Furthermore, the limitations of existing literature and the possible development trend are discussed.

不确定性理论成立于 2007 年，已成为数学的一个分支，用于模拟不确定性而非随机性。不确定图和不确定网络优化作为不确定性理论不可或缺的组成部分，受到了众多学者的广泛关注。当然，在不确定图和不确定网络优化方面也取得了一系列原创性的研究成果。本文旨在对不确定图和不确定网络优化的最新进展进行最先进的回顾。此外，它希望预测未来可能的研究方向。本文基于Web of Science数据库，检索2011年至2021年的144篇相关论文，分析发表文章的特征。更准确地说，我们分析了每年的出版物数量、关键主题和子领域、期刊和被引用次数最多的文章。此外，总结了不确定图和不确定网络优化的主要结果和模型。此外，讨论了现有文献的局限性和可能的​​发展趋势。