Persistent hypergraph homology and its applications

Abstract

Persistent homology theory is a relatively new but powerful method in data analysis. Using simplicial complexes, classical persistent homology is able to reveal high dimensional geometric structures of datasets, and represent them as persistent barcodes. However, many datasets contain complex systems of multi-way interactions, making these datasets more naturally and faithfully modeled by hypergraphs. In this article, we investigate the persistent hypergraph model, an important generalization of the classical persistent homology on simplicial complexes. We introduce a new homology, $\hat{H}$, on hypergraphs and an efficient algorithm to compute both persistent barcodes and $\hat{H}$ barcodes. As example, our theory is demonstrated by analyzing face-to-face interactions of different populations. The datasets that we select consist of baboons in primate center, people from rural Malawi, scientific conference, workplace and high school.