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Spectral GNNs' purported spectral advantages for node classification are illusory; their performance actually hinges on their underlying MPNN structure, debunking the "graph Fourier transform" narrative.
Diffusion models, despite their generative prowess, may not offer the silver-bullet privacy guarantees often assumed when synthesizing tabular data, as demonstrated by novel membership inference attacks.