Survey of Graph Neural Networks and Applications

Table 1

Notations.

Symbols	Descriptions
	The eigenvalue of the Laplacian matrix
	Feature matrix
	Graph with vectors and edges
	Degree matrix of vertices (diagonal matrix)
	Adjacency matrix of the graph
	Spectral input signal
	Spectral convolution kernel
	Self-learning parameter
	Diagonal matrix
	-dimensional vector
	Graph convolution
	Adjustable eigenvector matrix
	Chebyshev polynomial obeys
	Number of neighbors
	Attention factors
	Aggregation result of GAT