Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies

We introduce seven foundational principles for creating a culture ofconstructive criticism in computational legal studies. Beginning by challengingthe current perception of papers as the primary scholarly output, we call for amore comprehensive interpretation of publications. We then suggest to makethese publications computationally reproducible, releasing all of the data andall of the code all of the time, on time, and in the most functioning formpossible. Subsequently, we invite constructive criticism in all phases of thepublication life cycle. We posit that our proposals will help form our field,and float the idea of marking this maturity by the creation of a modernflagship publication outlet for computational legal studies.<br

Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework

Bridging geometry and topology, curvature is a powerful and expressiveinvariant. While the utility of curvature has been theoretically andempirically confirmed in the context of manifolds and graphs, itsgeneralization to the emerging domain of hypergraphs has remained largelyunexplored. On graphs, Ollivier-Ricci curvature measures differences betweenrandom walks via Wasserstein distances, thus grounding a geometric concept inideas from probability and optimal transport. We develop ORCHID, a flexibleframework generalizing Ollivier-Ricci curvature to hypergraphs, and prove thatthe resulting curvatures have favorable theoretical properties. Throughextensive experiments on synthetic and real-world hypergraphs from differentdomains, we demonstrate that ORCHID curvatures are both scalable and useful toperform a variety of hypergraph tasks in practice.<br

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

We introduce Hyperbard, a dataset of diverse relational data representationsderived from Shakespeare's plays. Our representations range from simple graphscapturing character co-occurrence in single scenes to hypergraphs encodingcomplex communication settings and character contributions as hyperedges withedge-specific node weights. By making multiple intuitive representationsreadily available for experimentation, we facilitate rigorous representationrobustness 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 solutionsto popular graph mining problems are highly dependent on the representationchoice, thus calling current graph curation practices into question. As anhomage to our data source, and asserting that science can also be art, wepresent all our points in the form of a play.<br

Differentially Describing Groups of Graphs

How does neural connectivity in autistic children differ from neuralconnectivity in healthy children or autistic youths? What patterns in globaltrade networks are shared across classes of goods, and how do these patternschange over time? Answering questions like these requires us to differentiallydescribe groups of graphs: Given a set of graphs and a partition of thesegraphs into groups, discover what graphs in one group have in common, how theysystematically differ from graphs in other groups, and how multiple groups ofgraphs are related. We refer to this task as graph group analysis, which seeksto describe similarities and differences between graph groups by means ofstatistically significant subgraphs. To perform graph group analysis, weintroduce Gragra, which uses maximum entropy modeling to identify anon-redundant set of subgraphs with statistically significant associations toone or more graph groups. Through an extensive set of experiments on a widerange of synthetic and real-world graph groups, we confirm that Gragra workswell in practice.<br

Juristische Netzwerkforschung. Modellierung, Quantifizierung und Visualisierung relationaler Daten im Recht

Netzwerke sind überall. Juristen nutzen sie auf dem Weg zur Arbeit (Infrastrukturnetzwerke), für die Suche nach Rat (Kontaktnetzwerke) und bei der juristischen Recherche (Informationsnetzwerke). Sie konstruieren sie (Zitiernetzwerke), beaufsichtigen sie (Finanznetzwerke) und bekämpfen sie (Verbrechensnetzwerke). Aus dieser Perspektive gibt es nichts, das sich nicht als Netzwerk modellieren lässt: als eine Menge von Einheiten, kombiniert mit einer Menge von Beziehungen zwischen diesen Einheiten. Corinna Coupette untersucht, wie juristische Phänomene als Netzwerke repräsentiert werden können, und ergründet, was man durch die quantitative und visuelle Analyse dieser Netzwerke für das Recht lernen kann. Dabei führt sie die juristische Netzwerkforschung in den deutschen juristischen Diskurs ein. Auf Basis eines eigens zusammengestellten Datensatzes von Entscheidungen des Bundesverfassungsgerichts entwickelt sie Werkzeuge zur Modellierung, Quantifizierung und Visualisierung des Rechts. Zur Arbeit gehört ein Online-Appendix, der unter folgender DOI abrufbar ist: https://doi.org/10.1628/978-3-16-157012-4-appendixLegal network science explores how legal phenomena can be represented as networks and investigates what can be gained from their quantification and visualization. Corinna Coupette introduces legal network science to the German legal discourse. Based on an original dataset of decisions by Germany's Federal Constitutional Court, she develops tools for modeling, measuring, and mapping the law