# Unifying local and non-local signal processing with graph CNNs

arXiv: https://arxiv.org/abs/1702.07759 · HTML: https://mlsanity.com/paper/1702.07759/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2017.

## Built on by (most cited first, 3 of 3)

- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [Generalization bounds for graph convolutional neural networks via Rademacher complexity](https://mlsanity.com/paper/2102.10234/) (2021)
- [Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency](https://mlsanity.com/paper/2104.09083/) (2021)

## Abstract (arXiv)

This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this article, we explain how this framework allows us to design a novel method to perform style transfer.