2 papers
eess.SP2025
On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to mul…
cs.LG2025
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Álvaro Arroyo, Alessio Gravina, Benjamin Gutteridge +5
Graph Neural Networks (GNNs) are models that leverage the graph structure to transmit information between nodes, typically through the message-passing operation. While widely succe…