Seminar Details

Network science has dominated analysis of complex relational data for decades. This is the practice of modeling data using a graph to represent pairwise relationships and then applying graph theoretic concepts--including degree distribution, diameter, centrality, and clustering--to understand the large scale structure of the graph/data. Network science has proven useful in a variety of domains including cyber security, bibliometrics, and computational biology. But in many of these cases the pairwise relationships that comprise the graph are inferred from more complex multi-way relationships. For example, a paper with more than two authors or a biological protein complex with more than two proteins. In these cases of multi-way relationships a hypergraph is a more accurate model of the data. In this talk I will describe the work my colleagues and I have been doing to develop theory, software, and use cases for hypernetwork science. I will introduce the relevant definitions and theoretical results to generalize network science concepts to hypergraphs and will close by showing some real examples using our HyperNetX Python package.