3 papers
cs.LG2026
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan +2
Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited ins…
eess.SP2026
On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
Reconstructing a signal on a graph from noisy observations of a subset of the vertices is a fundamental problem in the field of graph signal processing. This paper investigates how…
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…