Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach 事件

Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach arXiv:2605.29161v1 Announce Type: cross Abstract: Generating realistic graph-structured data is challenging due to discrete connectivity, varying graph sizes, and class-specific structural patterns. Recent Generative Adversarial Networks (GAN)-based graph generation methods improve edge modelling by learning connectivity and matching class-specific density distributions. However these models still exhibit noticeab