14 citations · 16 across the 9 of their papers we have counts for
6 papers · 1 filter
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher +18
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working…
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
Corinna Coupette, Jeremy Wayland, Emily Simons +1
Benchmark datasets have proved pivotal to the success of graph learning, and good benchmark datasets are crucial to guide the development of the field. Recent research has highligh…
Mapping the Multiverse of Latent Representations
Jeremy Wayland, Corinna Coupette, Bastian Rieck
Echoing recent calls to counter reliability and robustness concerns in machine learning via multiverse analysis, we present PRESTO, a principled framework for mapping the multivers…
Evaluating the "Learning on Graphs" Conference Experience
Bastian Rieck, Corinna Coupette
With machine learning conferences growing ever larger, and reviewing processes becoming increasingly elaborate, more data-driven insights into their workings are required. In this…
Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework
Corinna Coupette, Sebastian Dalleiger, Bastian Rieck
Bridging geometry and topology, curvature is a powerful and expressive invariant. While the utility of curvature has been theoretically and empirically confirmed in the context of…
All the World's a (Hyper)Graph: A Data Drama
Corinna Coupette, Jilles Vreeken, Bastian Rieck
We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-o…