Abstract: Recently, self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning, which aims to learn discriminative features for each node without ...
Abstract: In EEG-based connectivity analysis, multiple graphs are typically available, as obtained from measurements taken under different conditions, such as frequency bands, trials, or patients’ ...
This repository is the implementation of the following paper: Theoretical Insights into Line Graph Transformation on Graph Learning. This project is built on the BREC dataset which includes 400 pairs ...
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