289 citations · 498 across the 15 of their papers we have counts for
16 papers
How Faithful are Self-Explainable GNNs?
Marc Christiansen, Lea Villadsen, Zhiqiang Zhong +2
Self-explainable deep neural networks are a recent class of models that can output ante-hoc local explanations that are faithful to the model's reasoning, and as such represent a s…
ActUp: Analyzing and Consolidating tSNE and UMAP
Andrew Draganov, Jakob Rødsgaard Jørgensen, Katrine Scheel Nellemann +4
tSNE and UMAP are popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. Despite their popularity, however, little work has be…
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
Zhiqiang Zhong, Anastasia Barkova, Davide Mottin
The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models a…
Spectral Graph Complexity
Anton Tsitsulin, Davide Mottin, Panagiotis Karras +2
We introduce a spectral notion of graph complexity derived from the Weyl's law. We experimentally demonstrate its correlation to how well the graph can be embedded in a low-dimensi…
GiDR-DUN; Gradient Dimensionality Reduction -- Differences and Unification
Andrew Draganov, Tyrus Berry, Jakob Rødsgaard Jørgensen +3
TSNE and UMAP are two of the most popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. However, while attempts have been mad…
UCoDe: Unified Community Detection with Graph Convolutional Networks
Atefeh Moradan, Andrew Draganov, Davide Mottin +1
Community detection finds homogeneous groups of nodes in a graph. Existing approaches either partition the graph into disjoint, non-overlapping, communities, or determine only over…