Position: Spectral GNNs Are Neither Spectral Nor Superior for Node Classification | ScienceToStartup | ScienceToStartup

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References (35)

[1]

Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration

2025Yonghao Liu, Yajun Wang et al.

[2]

Demystifying MPNNs: Message Passing as Merely Efficient Matrix Multiplication

2025Qin Jiang, Chengjia Wang et al.

[3]

Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived

Founder's Pitch

"This paper argues that Spectral GNNs for node classification are theoretically flawed and their performance gains are not due to spectral properties but rather message-passing dynamics."

Graph Neural Networks•Score: 3•View PDF ↗

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Position: Spectral GNNs Are Neither Spectral Nor Superior for Node Classification

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References (35)

Founder's Pitch

"This paper argues that Spectral GNNs for node classification are theoretically flawed and their performance gains are not due to spectral properties but rather message-passing dynamics."

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