1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025★ 1 cited
Guided Graph Compression for Quantum Graph Neural Networks
Mikel Casals, Vasilis Belis, Elias F. Combarro +3
Graph Neural Networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix…
cs.LG2023
Towards a more inductive world for drug repurposing approaches
Jesus de la Fuente, Guillermo Serrano, Uxía Veleiro +8
Drug-target interaction (DTI) prediction is a challenging, albeit essential task in drug repurposing. Learning on graph models have drawn special attention as they can significantl…