  Many edge prediction methods have been proposed, based on various local or
global properties of the structure of an incomplete network. Community
structure is another significant feature of networks: Vertices in a community
are more densely connected than average. It is often true that vertices in the
same community have "similar" properties, which suggests that missing edges are
more likely to be found within communities than elsewhere. We use this insight
to propose a strategy for edge prediction that combines existing edge
prediction methods with community detection. We show that this method gives
better prediction accuracy than existing edge prediction methods alone.
