  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}.
