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This work shows for the first time that graph diffusion models indeed exhibit memorization, and identifies a strong train-data bias that coincides with a growing generalization gap.
Discrete graph diffusion models corrupt graph structure through independent edge updates with a Markovian noise model. Combined with the exchangeability requirement on the output distribution, the terminal prior is necessarily an Erd\H{o}s--R\'enyi (E-R) random graph. This has a fundamental consequence for unconditiona...
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