Spectral Graph Convolutions

In this tutorial, we will focus on the mathematical foundations of the first successful GNN method: Graph Convolutional Networks (GCN).

[2012.06660] Understanding Spectral Graph Neural Network - arXiv

It illustrates how the graph convolutional neural network model is motivated by spectral graph theory, and discusses the major spectral-based models associated ...

Understanding Convolutions on Graphs - Distill.pub

Spectral Convolutions are Node-Order Equivariant. We can show spectral convolutions are node-order equivariant using a similar approach as for ...

What is the difference between graph convolution in the spatial vs ...

Unlike Spectral Convolution which takes a lot of time to compute, Spatial Convolutions are simple and have produced state of the art results on ...

SIMPLE SPECTRAL GRAPH CONVOLUTION - OpenReview

Vanila Graph Convolutional Network (GCN) (Kipf & Welling, 2016). The vanilla GCN is a first-order approximation of spectral graph convolutions. If one sets ...

Simple Spectral Graph Convolution - OpenReview

A simple and efficient method for graph convolution based on the Markov Diffusion Kernel, which works well on different tasks under unsupervised, semi- ...

Spectral Heterogeneous Graph Convolutions via Positive ... - arXiv

We present a positive spectral heterogeneous graph convolution via positive noncommutative polynomials. Then, using this convolution, we propose PSHGCN.

Week 13 – Lecture: Graph Convolutional Networks (GCNs) - YouTube

Then we extend to the graph domain. We understand the characteristics of graph and define the graph convolution. Finally, we introduce spectral ...

Transferability of Spectral Graph Convolutional Neural Networks

When generalizing this architecture to graphs, there is a need to extend the convolution, activation function, and pooling to graph structured data. Here, graph ...

Graph Convolution - an overview | ScienceDirect Topics

The Convolutional architecture is based on spectral graph convolutions and choosing their local first-order approximations. The authors have considered the ...

Simple Spectral Graph Convolution - Papers With Code

In this paper, we use the Markov diffusion kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S^2GC) which is closely related to ...

Spectral Graph Convolutional Neural Networks Do Generalize

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Metric learning with spectral graph convolutions on brain ...

We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting.

Metric learning with spectral graph convolutions on brain ... - PubMed

We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting.

Spectral graph convolutional network, re-assigning indices

Graph convolutions are equivariant under permutations, ie, permutation and convolution commute. That's desirable as the ordering of vertices is arbitrary.

How powerful are Graph Convolutions? (review of Kipf & Welling ...

Fourier-transforms do generalise to graphs, therefore we can define a general concept of graph convolutions as pointwise multiplication of the ...

Graph convolutional networks: a comprehensive review

As the spectral graph convolution relies on the specific eigenfunctions of Laplacian matrix, it is still nontrivial to transfer the spectral- ...

Spectral graph convolutions - Notes on AI

Spectral graph convolutions What is the 'shift' in graphs? What is the equivalent of fourier basis for graphs? Solution: Eigen vectors of Graphs > Graph ...

Spectral Graph Convolutions for Population-Based Disease Prediction

The concept of spectral graph convolutions exploits the fact that convolutions are multiplications in the Fourier domain. The graph Fourier ...

From Spectral Graph Convolutions to Large Scale ... - ResearchGate

In this work we study the theory that paved the way to the definition of GCN, including related parts of classical graph theory.