Missing data imputation poses a paramount challenge when dealing with graph data. Prior works typically are based on feature propagation or graph autoencoders to address this issue. However, these methods usually encounter the over-smoothing issue when dealing with missing data, as the graph neural network (GNN) modules are not explicitly designed for handling missing data. This paper proposes a novel framework, called Dual-Path Generative Adversarial Network (DPGAN), that can deal simultaneously with missing data and avoid over-smoothing problems. The crux of our work is that it admits both global and local representations of the input graph signal, which can capture the long-range dependencies. It is realized via our proposed generator, consisting of two key components, i.e., MLPUNet++ and GraphUNet++. Our generator is trained with a designated discriminator via an adversarial process. In particular, to avoid assessing the entire graph as did in the literature, our discriminator focuses on the local subgraph fidelity, thereby boosting the quality of the local imputation. The subgraph size is adjustable, allowing for control over the intensity of adversarial regularization. Comprehensive experiments across various benchmark datasets substantiate that DPGAN consistently rivals, if not outperforms, existing state-of-the-art imputation algorithms. The code is provided at \url{https://github.com/momoxia/DPGAN}.

翻译：图数据中的缺失值填补是一项关键挑战。现有方法通常基于特征传播或图自编码器来解决该问题，但由于图神经网络模块并非专门针对缺失数据设计，这些方法在处理缺失数据时常面临过平滑问题。本文提出了一种名为双路径生成对抗网络（DPGAN）的新框架，可同时处理缺失数据并避免过平滑问题。本工作的核心在于该框架能够同时利用输入图信号的全局与局部表征，从而捕获长程依赖关系。该功能通过我们提出的生成器实现，其包含两个关键组件：MLPUNet++与GraphUNet++。该生成器通过对抗过程与指定判别器协同训练。特别地，为规避现有文献中对全图进行判别的方式，我们的判别器聚焦于局部子图保真度，从而提升局部填补质量。子图尺寸可调节，允许对对抗正则化强度进行控制。在多个基准数据集上的综合实验表明，DPGAN始终能与现有最优填补算法相媲美，甚至更优。相关代码见\url{https://github.com/momoxia/DPGAN}。