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cs.LG2024
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Naheed Anjum Arafat, Debabrota Basu, Yulia Gel +1
Capitalizing on the intuitive premise that shape characteristics are more robust to perturbations, we bridge adversarial graph learning with the emerging tools from computational t…
cs.LG2024
TopoGCL: Topological Graph Contrastive Learning
Yuzhou Chen, Jose Frias, Yulia R. Gel
Graph contrastive learning (GCL) has recently emerged as a new concept which allows for capitalizing on the strengths of graph neural networks (GNNs) to learn rich representations…