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cs.LG2026
Diversity Curves for Graph Representation Learning
Katharina Limbeck, Nadja Häusermann, Martin Carrasco +2
Graph-level representations are crucial tools for characterising structural differences between graphs. However, comparing graphs with different cardinalities, even when sampled fr…
cs.LG2025
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…
cs.LG2023
Metric Space Magnitude for Evaluating the Diversity of Latent Representations
Katharina Limbeck, Rayna Andreeva, Rik Sarkar +1
The magnitude of a metric space is a novel invariant that provides a measure of the 'effective size' of a space across multiple scales, while also capturing numerous geometrical pr…