6 citations · 9 across the 20 of their papers we have counts for
5 papers · 1 filter
Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs
Martin Carrasco, Olga Zaghen, Kavir Sumaraj +2
A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing appr…
EmbedOR: Provable Cluster-Preserving Visualizations with Curvature-Based Stochastic Neighbor Embeddings
Tristan Luca Saidi, Abigail Hickok, Bastian Rieck +1
Stochastic Neighbor Embedding (SNE) algorithms like UMAP and tSNE often produce visualizations that do not preserve the geometry of noisy and high dimensional data. In particular,…
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…
Geometry-Aware Edge Pooling for Graph Neural Networks
Katharina Limbeck, Lydia Mezrag, Guy Wolf +1
Graph Neural Networks (GNNs) have shown significant success for graph-based tasks. Motivated by the prevalence of large datasets in real-world applications, pooling layers are cruc…
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…