  \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.
