Title: Patch-level Neighborhood Interpolation:  A General and Effective Graph-based Regularization Strategy. Abstract: Regularization plays a crucial role in machine learning models, especially for deep neural networks. The existing regularization techniques mainly rely on the i.i.d. assumption and only consider the knowledge from the current sample, without the leverage of the neighboring relationship between samples. In this work, we propose a general regularizer called Patch-level Neighborhood Interpolation~(\textbf{Pani}) that conducts a non-local representation in the computation of network. Our proposal explicitly constructs patch-level graphs in different network layers and then linearly interpolates neighborhood patch features, serving as a general and effective regularization strategy. Further, we customize our approach into two kinds of popular regularization methods, namely Virtual Adversarial Training (VAT) and MixUp as well as its variants. The first derived \textbf{Pani VAT} presents a novel way to construct non-local adversarial smoothness by employing patch-level interpolated perturbations. In addition, the second derived \textbf{Pani MixUp} method extends the original MixUp regularization and its variant to the Pani version, achieving a  significant improvement in the performance. Finally, extensive experiments are conducted to verify the effectiveness of our Patch-level Neighborhood Interpolation approach in both supervised and semi-supervised settings.