Dynamic Local Filters in Graph Convolutional Neural Networks

Over the last few years, we have seen increasing data generated from non-Euclidean domains, usually represented as graphs with complex relationships. Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. In particular, there is a strong interest in performing convolution on graphs using specific GNN architectures, generally called Graph Convolutional Neural Networks (GCNN). This paper presents a novel method to adapt the behaviour of a GCNN using an input-based dynamically generated filter. Notice that the idea of adapting the network behaviour to the inputs they process to maximize the total performances has aroused much interest in the neural networks literature over the years. The experimental assessment confirms the capabilities of the proposed approach, achieving promising results using simple architectures with a low number of filters.