layer_graph_conv_skip: GraphConvSkip
In rdinnager/rspektral: What the Package Does (One Line, Title Case)

\loadmathjax

A simple convolutional layer with a skip connection.

Mode: single, disjoint, mixed, batch.

This layer computes: \mjdeqn Z = D^-1/2 A D^-1/2 X W_1 + X W_2 + b where \mjeqn A does not have self-loops (unlike in GraphConv).

Input

Node features of shape ([batch], N, F);
Normalized adjacency matrix of shape ([batch], N, N); can be computed with spektral.utils.convolution.normalized_adjacency.

Output

Node features with the same shape as the input, but with the last dimension changed to channels.

layer_graph_conv_skip(
  object,
  channels,
  activation = NULL,
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  bias_initializer = "zeros",
  kernel_regularizer = NULL,
  bias_regularizer = NULL,
  activity_regularizer = NULL,
  kernel_constraint = NULL,
  bias_constraint = NULL,
  ...
)

`channels`	number of output channels
`activation`	activation function to use
`use_bias`	bool, add a bias vector to the output
`kernel_initializer`	initializer for the weights
`bias_initializer`	initializer for the bias vector
`kernel_regularizer`	regularization applied to the weights
`bias_regularizer`	regularization applied to the bias vector
`activity_regularizer`	regularization applied to the output
`kernel_constraint`	constraint applied to the weights
`bias_constraint`	constraint applied to the bias vector.