\emph{Uncertain Graph} (also known as \emph{Probabilistic Graph}) is a generic model to represent many real\mbox{-}world networks from social to biological. In recent times analysis and mining of uncertain graphs have drawn significant attention from the researchers of the data management community. Several noble problems have been introduced and efficient methodologies have been developed to solve those problems. Hence, there is a need to summarize the existing results on this topic in a self\mbox{-}organized way. In this paper, we present a comprehensive survey on uncertain graph mining focusing on mainly three aspects: (i) different problems studied, (ii) computational challenges for solving those problems, and (iii) proposed methodologies. Finally, we list out important future research directions.

中文翻译：

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不确定图挖掘与分析综述

\emph{Uncertain Graph}（也称为 \emph{Probabilistic Graph}）是一个通用模型，用于表示从社会到生物的许多真实\mbox{-}世界网络。最近，不确定图的分析和挖掘引起了数据管理社区研究人员的极大关注。已经引入了几个重要的问题，并且已经开发了有效的方法来解决这些问题。因此，有必要以自\mbox{-} 组织的方式总结关于该主题的现有结果。在本文中，我们对不确定图挖掘进行了全面调查，主要关注三个方面：（i）研究的不同问题，（ii）解决这些问题的计算挑战，以及（iii）提出的方法。最后，我们列出了未来重要的研究方向。