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Overall, the results show that SNN design choice, evaluation protocol, spike-timing representation, and computational activity should be examined separately when applying SNNs to static network-flow data.
This survey serves as a foundational reference for advancing generative AI techniques across the audio domain, and is the first work to jointly cover all three audio domains and all generative model families, while also providing dedicated evaluation-metric analysis and cross-domain comparative assessments.
This work demonstrates that an adversary can use the generator-discriminator technique to reconstruct high-quality graphs in real-world black-box attack scenarios against GNNs, and shows that GNNs are highly vulnerable to privacy attacks, varying Laplacian noise-scales.
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