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cs.LG2026
ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation
Salvatore Romano, Marco Grassia, Pietro Liò +1
Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the c…
cs.LG2026
Beyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning
Salvatore Romano, Marco Grassia, Giuseppe Mangioni
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-wo…