Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation learning. Specifically, we design an adversarially trained encoder that learns an invariant structural representation against diverse perturbations and derives a statistically independent watermark carrier, ensuring both robustness and transparency of watermarks. Meanwhile, we devise a graph-aware invertible neural network to provide a lossless channel for watermark embedding and extraction, guaranteeing high detectability and transparency of watermarks. Additionally, we develop a structure-aware editor that resolves the issue of latent modifications into discrete graph edits, ensuring robustness against structural perturbations. Experiments on diverse benchmark datasets demonstrate the superior effectiveness of DRGW.

翻译：图结构数据是众多网络应用的基础，而水印技术对于保护其知识产权和确保数据来源至关重要。现有的水印方法主要在图结构或纠缠的图表示上操作，由于图表示中的信息耦合以及将连续数值表示转换为图结构时不可控的离散化，这些方法会损害水印的透明性和鲁棒性。这促使我们提出DRGW，首个通过分离表示学习来解决这些问题的图水印框架。具体而言，我们设计了一个对抗训练的编码器，该编码器学习针对多种扰动的不变结构表示，并推导出一个统计独立的水印载体，从而确保水印的鲁棒性和透明性。同时，我们设计了一个图感知的可逆神经网络，为水印嵌入和提取提供一个无损通道，保证了水印的高可检测性和透明性。此外，我们开发了一个结构感知编辑器，将潜在修改问题转化为离散的图编辑操作，确保了对结构扰动的鲁棒性。在多个基准数据集上的实验证明了DRGW的卓越有效性。