GraphK：具备高效边构建能力的可变大小图生成方法
GraphK: Variable-Size Graph Generation with Efficient Edge Construction
- Ataturk University（阿塔图尔克大学）
- Istanbul Technical University（伊斯坦布尔理工大学）
- Memorial University（纪念大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出新型图生成框架GraphK，通过编码器-采样器-解码器结构及KDTree边预测实现可变大小图生成，在合成与真实数据集上性能优于现有方法。
AI中文摘要：
图生成模型在深度学习的推动下已取得显著进展，但在可扩展性、灵活性以及对底层结构的建模能力方面仍存在局限。我们提出了GraphK，一种用于图生成的新型编码器-采样器-解码器框架，其通过结构灵活性和计算效率克服了这些挑战。与受词汇量（即图生成中的节点数量）限制的自回归方法不同，GraphK支持放大（生成比输入节点更多的图）和缩小，可灵活控制输出图的大小。通过学习置换不变的潜在表示并利用最大似然估计采样新节点嵌入，GraphK可在不同图大小和结构间泛化。对于边生成，我们采用基于KDTree的潜在空间top-k近邻搜索的边预测，降低了计算成本。基于流形平滑假设，我们的方法能有效捕捉图属性。在合成数据集和真实世界数据集上的实验表明，GraphK的性能优于现有方法，可准确学习图结构并生成无需明确定义的合成图。
英文摘要：
Graph generation models have advanced significantly with deep learning, yet they remain limited in scalability, flexibility, and ability to model underlying structures. We present GraphK, a novel encoder-sampler-decoder framework for graph generation that overcomes these challenges through structural flexibility and computational efficiency. Unlike autoregressive approaches constrained by vocabulary size (i.e. number of nodes in graph generation), GraphK allows for both upscaling (generating graphs with more nodes than the input) and downscaling, providing a flexible control over output graph size. By learning permutation-invariant latent representations and sampling new node embeddings via maximum likelihood estimation, GraphK generalizes across graph sizes and structures. For edge generation, we employ edge prediction with a KDTree-based top-k neighbor search in the latent space, reducing computational cost. Based on the manifold smoothness assumption, our method effectively captures graph properties. Experiments on synthetic and real-world datasets show that GraphK outperforms existing methods, accurately learns graph structures, and generates synthetic graphs without explicit definitions.
