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09/04/2023
On the Robustness of Post-hoc GNN Explainers to Label Noise
Proposed as a solution to the inherent black-box limitations of graph ne...
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08/29/2023
How Faithful are Self-Explainable GNNs?
Self-explainable deep neural networks are a recent class of models that ...
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05/12/2023
ActUp: Analyzing and Consolidating tSNE and UMAP
tSNE and UMAP are popular dimensionality reduction algorithms due to the...
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02/16/2023
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
The integration of Artificial Intelligence (AI) into the field of drug d...
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06/20/2022
GiDR-DUN; Gradient Dimensionality Reduction – Differences and Unification
TSNE and UMAP are two of the most popular dimensionality reduction algor...
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06/10/2021
GRASP: Graph Alignment through Spectral Signatures
What is the best way to match the nodes of two graphs? This graph alignm...
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03/11/2021
SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs
Analytical queries over RDF data are becoming prominent as a result of t...
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02/07/2021
Bandits for Learning to Explain from Explanations
We introduce Explearn, an online algorithm that learns to jointly output...
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09/29/2020
What if Neural Networks had SVDs?
Various Neural Networks employ time-consuming matrix operations like mat...
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06/08/2020
FREDE: Linear-Space Anytime Graph Embeddings
Low-dimensional representations, or embeddings, of a graph's nodes facil...
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05/27/2019
Intrinsic Multi-scale Evaluation of Generative Models
Generative models are often used to sample high-dimensional data points ...
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11/15/2018
SGR: Self-Supervised Spectral Graph Representation Learning
Representing a graph as a vector is a challenging task; ideally, the rep...
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03/13/2018
VERSE: Versatile Graph Embeddings from Similarity Measures
Embedding a web-scale information network into a low-dimensional vector ...
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02/12/2018