Proposing Ties in a Dense Hypergraph of Academics

Nearly all personal relationships exhibit a multiplexity where people relate to one another in many different ways. Using a set of faculty CVs from multiple research institutions, we mined a hypergraph of researchers connected by co-occurring named entities (people, places and organizations). This results in an edge-sparse, link-dense structure with weighted connections that accurately encodes faculty department structure. We introduce a novel model that generates dyadic proposals of how well two nodes should be connected based on both the mass and distributional similarity of links through shared neighbors. Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial. Our model is tested by using its proposals to recover link strengths from four systematically lesioned versions of the graph. The model is also compared to other link prediction methods in a static setting. Our results show the model is able to recover a majority of link mass in various settings and that it out-performs other link prediction methods. Overall, the results support the descriptive fidelity of our text-mined, named entity hypergraph of multi-faceted relationships and underscore the importance of link similarity in analyzing link-dense multiplexitous relationships

On the analysis of time-varying affiliation networks: The case of stage co-productions

Multiple Correspondence Analysis and Multiple Factor Analysis have proved appropriate for visually analyzing affiliation (two-mode) networks. However, more could be said about the use of these tools within the positional approach of social network analysis, relying upon the ways in which both these factorial methods and blockmodeling can lead to an appraisal of positional equivalences. This paper presents a joint approach that combines all these methods in order to perform a positional analysis of time-varying affiliation networks. We present an application to an affiliation network of theatre companies involved in stage co-productions over four seasons. The study shows how the joint use of Multiple Factor Analysis and blockmodeling helps us understand network positions and the longitudinal affiliation patterns characterizing them