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Researchers propose a lightweight autoregressive framework for graph generation that uses structure-guided topological ordering to achieve near log-linear complexity, addressing scalability and novelty limitations of existing diffusion and autoregressive methods. The approach supports both LSTM and Mamba-style backbones and shows improved novelty while maintaining validity and uniqueness on molecular and non-molecular benchmarks.
This paper proposes a hybrid WGAN-GA approach for refining generative graph topologies, using a genetic algorithm to correct residual structural deviations in GAN-based generated graphs, improving realism for synthetic graph synthesis and data augmentation.