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# Applied Math Seminar: Juergen Kritschgau (Carnegie Mellon)

## November 28 @ 4:15 pm - 5:15 pm

Title: Using Mutual Information of Hypergraph Compressions for Clustering

Abstract: Hypergraphs are often used to represent higher order observed relationships between subjects of study. In particular, the vertices of a hypergraph could represent the basic elements of study, and edges represent observed relationships between the vertices. Implicitly, the assumption is that observed edges are more (or less) likely to appear between vertices that are “similar”. Therefore, an important question in data science is whether the edges of a hypergraph can be used to recover ground truth vertex labels where two vertices receive the same label if they are similar. This is known as the clustering problem. In this talk, we will discuss how mutual information of hypergraph compressions can be used to cluster hypergraphs, and apply this clustering strategy to synthetic and real world data sets.