Heterogeneous Hypergraphs Models for Collaboration Networks: A Bridge between Mathematics and Business

Hypergraphs models are able to capture additional features in comparison to general graph models and have the potential to fruitfully be applied in many different fields. Nevertheless, many fields are not familiar with the concept of hypergraphs. This results mainly from the fact that hypergraph models are hard to understand due to the difficult mathematics involved and the use of inconsistent notation. This is especially true in business fields, since these fields are less familiar with abstract mathematics, even though hypergraphs might be particularly useful in these fields where networks of collaboration exists. In this thesis we will fill this gap by building a bridge between mathematics and business. The following research question is answered: How could mathematical heterogeneous hypergraph models be helpful in the field of collaboration networks in business? In order to address this question, we first show which mathematical hypergraph models represent collaboration networks best. For this purpose different existing hypergraph models are compared and changed into heterogeneous hypergraph models with heterogeneous node types. The models will be evaluated using data on R&D collaboration. Furthermore, we show how these models could easily be applied in business by providing clear notation, accessible hypergraph models and results for business.