@inproceedings{Tran-2018-filter,
author = {Tran, Dinh V and Navarin, Nicolò and Sperduti, Alessandro},
title = {On filter size in graph convolutional networks},
booktitle = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)},
year = {2018},
doi = {https://arxiv.org/pdf/1811.10435.pdf},
user = {miladim},
pages = {1534--1541},
organization = {IEEE},
abstract = {
            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.}
}