Graph Kernel Neural Networks

12/14/2021
by   Luca Cosmo, et al.
194

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more challenging, due to their irregular structure. In this paper, we propose to use graph kernels, i.e., kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type and number of graph kernels and has the added benefit of providing some interpretability in terms of the structural masks that are learned during the training process, similarly to what happens for convolutional masks in traditional convolutional neural networks. We perform an extensive ablation study to investigate the impact of the model hyper-parameters and we show that our model achieves competitive performance on standard graph classification datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/24/2017

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

We present Spline-based Convolutional Neural Networks (SplineCNNs), a va...
research
12/10/2022

QESK: Quantum-based Entropic Subtree Kernels for Graph Classification

In this paper, we propose a novel graph kernel, namely the Quantum-based...
research
07/18/2022

SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data

Convolutional Neural Networks have revolutionized vision applications. T...
research
06/08/2017

Generalized Value Iteration Networks: Life Beyond Lattices

In this paper, we introduce a generalized value iteration network (GVIN)...
research
07/20/2023

QDC: Quantum Diffusion Convolution Kernels on Graphs

Graph convolutional neural networks (GCNs) operate by aggregating messag...
research
04/22/2018

Decoupled Networks

Inner product-based convolution has been a central component of convolut...
research
03/02/2021

Graph-Time Convolutional Neural Networks

Spatiotemporal data can be represented as a process over a graph, which ...

Please sign up or login with your details

Forgot password? Click here to reset