  Characterizing motif (i.e., locally connected subgraph patterns) statistics
is important for understanding complex networks such as online social networks
and communication networks. Previous work made the strong assumption that the
graph topology of interest is known, and that the dataset either fits into main
memory or stored on disks such that it is not expensive to obtain all neighbors
of any given node. In practice, researchers have to deal with the situation
where the graph topology is unknown, either because the graph is dynamic, or
because it is expensive to collect and store all topological and meta
information on disk. Hence, what is available to researchers is only a snapshot
of the graph generated by sampling edges from the graph at random, which we
called a "RESampled graph". Clearly, a RESampled graph's motif statistics may
be quite different from the underlying original graph. To solve this challenge,
we propose a framework and implement a system called Minfer, which can take the
given RESampled graph and accurately infer the underlying graph's motif
statistics. We also use Fisher information to bound the error of our estimates.
Experiments using large scale datasets show that our method to be accurate.
