Dylan Sandfelder

2 accepted papers

2026

DATA-DRIVEN GRAPH FILTERS VIA ADAPTIVE SPECTRAL SHAPING

Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong

ICASSP 2026poster

We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering that learns a reusable baseline spectral kernel and modulates it with a small set of Gaussian factors. The resulting multi-peak, multi-scale responses allocate energy to heterogeneous regions of the Laplacian spectru…

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2021

Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks

Dylan Sandfelder, Priyesh Vijayan, William L. Hamilton

ICASSP 2021accepted

Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-structured inductive bias: the WL-subtree kernel formulation bounds the representational capacity of GNNs, and polynomial-tim…

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