AI RESEARCH
Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach
arXiv CS.AI
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ArXi:2605.29161v1 Announce Type: cross 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 noticeable deviations such as in degree and spectral distribution when compared to real graphs, indicating that important structural properties are not fully preserved.