# On Filter Size in Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1811.10435 · HTML: https://mlsanity.com/paper/1811.10435/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2018.

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

- [Pyramidal Reservoir Graph Neural Network](https://mlsanity.com/paper/2104.04710/) (2021)
- [Simple Graph Convolutional Networks](https://mlsanity.com/paper/2106.05809/) (2021)
- [Modeling Edge Features with Deep Bayesian Graph Networks](https://mlsanity.com/paper/2308.09087/) (2023)

## Abstract (arXiv)

Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. In particular, we derive a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph. We show with experimental results on real-world graph datasets that the proposed graph convolutional filter improves the predictive performance of Deep Graph Convolutional Networks.