  In many real world datasets arising from social networks, there are hidden
higher order relations among data points which cannot be captured using graph
modeling. It is natural to use a more general notion of hypergraphs to model
such social networks. In this paper, we introduce a new local geometry of
hyperdges in hypergraphs which allows to capture higher order relations among
data points. Furthermore based on this new geometry, we also introduce new
methodology--the nearest neighbors method in hypergraphs--for analyzing
datasets arising from sociology.
