All the World's a (Hyper)Graph: A Data Drama

TL;DR

Hyperbard is a diverse dataset of relational data representations from Shakespeare's plays, enabling analysis of how different graph formats affect graph mining and network analysis outcomes.

Contribution

The paper introduces Hyperbard, a novel dataset of multiple relational representations from Shakespeare's plays, facilitating robustness checks and highlighting representation-dependent results.

Findings

Representation choice significantly impacts graph mining results.

Multiple data representations reveal advantages and drawbacks in analysis.

Current graph curation practices may need reevaluation.

Abstract

We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings and character contributions as hyperedges with edge-specific node weights. By making multiple intuitive representations readily available for experimentation, we facilitate rigorous representation robustness checks in graph learning, graph mining, and network analysis, highlighting the advantages and drawbacks of specific representations. Leveraging the data released in Hyperbard, we demonstrate that many solutions to popular graph mining problems are highly dependent on the representation choice, thus calling current graph curation practices into question. As an homage to our data source, and asserting that science can also be art, we present all our points in the form of a play.