Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties. Existing methods often fall short in efficiently addressing this need as they struggle with the high-dimensional complexity and varied nature of graph properties. In this paper, we introduce the Neural Graph Generator (NGG), a novel approach which utilizes conditioned latent diffusion models for graph generation. NGG demonstrates a remarkable capacity to model complex graph patterns, offering control over the graph generation process. NGG employs a variational graph autoencoder for graph compression and a diffusion process in the latent vector space, guided by vectors summarizing graph statistics. We demonstrate NGG's versatility across various graph generation tasks, showing its capability to capture desired graph properties and generalize to unseen graphs. This work signifies a significant shift in graph generation methodologies, offering a more practical and efficient solution for generating diverse types of graphs with specific characteristics.

翻译：图生成已成为机器学习中的关键任务，但在生成能够准确反映特定属性的图结构时面临重大挑战。现有方法常因难以应对图属性的高维复杂性和多样性，而无法高效满足这一需求。本文提出神经图生成器（NGG）——一种利用条件潜在扩散模型进行图生成的新颖方法。NGG展现出建模复杂图模式的卓越能力，可对图生成过程实现可控性。该模型采用变分图自编码器进行图压缩，并在潜在向量空间中执行扩散过程，该过程由汇总图统计特征的向量引导。我们展示了NGG在多种图生成任务中的通用性，证明其既能捕获目标图属性，又可泛化至未见图结构。本研究标志着图生成方法论的重大转变，为生成具有特定属性的多样化图结构提供了更实用、高效的解决方案。