Spectral convolution methods perform graph Fourier transformation on nodes and map to spectral domain, which involved conducting eigenvalue decomposition of graph Laplacian matrix.

Spectral Convolution Methods

It performs convolution among neighbor nodes, which is based on connection relationship between nodes.

Spatial Convolution

Convolution operations can be divided into two types:

1)	Spectral convolution

2)	Spatial convolution

3)	Mixture of spectral convolution and special convolution


University of Notre Dame

GCN uses convolution operation and readout operation to grasp node level and graph level patterns.

Graph Convolutional Networks (GCN)

Zhang, Z., Cui, P., & Zhu, W. (2020). Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering.

Deep Learning on Graphs: A Survey

Types of Convolution Operation

1)	Attention Mechanism

2)	Residual and Jumping Connections

3)	Utilizing Edge Features

4)	Sampling Methods

5)	Inductive Setting

6)	Theoretical Analysis


Possible Improvements of GCN

When a graph-level representation is needed to tackle graph-level tasks, node-level representations can be aggregated into a single graph-level representation. This aggregation process is referred to as a readout operation.

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