Structure aware graph generation aims to generate graphs that satisfy given topological properties. It has applications in domains such as drug discovery, social network modeling, and knowledge graph construction. Unlike existing methods that only provide coarse control over graph properties, we introduce a novel conditional variational autoencoder for fine-grained structural control in graph generation. The approach refines the decoder's latent space by dynamically aligning graph- and property-driven representations to improve both graph fidelity and control satisfaction. Specifically, the approach implements a mixture scheduler that progressively integrates graph and control priors. Experiments on five real-world datasets show the efficacy of the proposed model compared to recent baselines, achieving high generation quality while maintaining high controllability.

翻译：结构感知图生成旨在生成满足给定拓扑性质的图。它在药物发现、社交网络建模和知识图谱构建等领域具有应用价值。与现有仅能对图属性提供粗粒度控制的方法不同，我们引入了一种新颖的条件变分自编码器，以实现图生成中的细粒度结构控制。该方法通过动态对齐图驱动表示与属性驱动表示来优化解码器的潜在空间，从而提升图的保真度与控制满足度。具体而言，该方法实现了一种混合调度器，能够逐步整合图先验与控制先验。在五个真实世界数据集上的实验表明，与近期基线方法相比，所提模型在保持高生成质量的同时实现了高可控性。