A local geometry of hyperedges in hypergraphs, and its applications to social networks
This work addresses the limitation of graph models in capturing complex social interactions, offering a novel approach for sociology datasets.
The authors tackled the problem of modeling hidden higher-order relations in social network data by introducing a new local geometry for hyperedges in hypergraphs, and they developed a nearest neighbors method for analyzing such datasets.
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.