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.

介绍了 Hyperbard 数据集，包括从莎士比亚戏剧中提取的多种关系型数据表示形式，利用该数据集可以在图学习、图挖掘和网络分析等领域进行多种表现形式的实验。利用 Hyperbard 的数据，证明了许多通用图挖掘问题的解决方案取决于表示形式的选择，从而质疑了当前图数据的采集方式。