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05/26/2022
DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees
We propose the fully explainable Decision Tree Graph Neural Network (DT+...
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05/24/2022
Asynchronous Neural Networks for Learning in Graphs
This paper studies asynchronous message passing (AMP), a new paradigm fo...
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11/11/2021
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
This paper studies Dropout Graph Neural Networks (DropGNNs), a new appro...
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03/11/2021
Should Graph Neural Networks Use Features, Edges, Or Both?
Graph Neural Networks (GNNs) are the first choice for learning algorithm...
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02/25/2021
Towards Robust Graph Contrastive Learning
We study the problem of adversarially robust self-supervised learning on...
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10/26/2020
Contrastive Graph Neural Network Explanation
Graph Neural Networks achieve remarkable results on problems with struct...
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08/25/2020
Medley2K: A Dataset of Medley Transitions
The automatic generation of medleys, i.e., musical pieces formed by diff...
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04/15/2020
