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

\loadmathjax

A graph convolutional layer implementing the APPNP operator, as presented by Klicpera et al. (2019).

This layer computes: \mjdeqn Z^(0) = \textrmMLP(X); \ Z^(K) = (1 - \alpha) \hat D^-1/2 \hat A \hat D^-1/2 Z^(K - 1) + \alpha Z^(0), where \mjeqn\alpha is the teleport probability and \mjeqn\textrmMLP is a multi-layer perceptron.

Mode: single, disjoint, mixed, batch.

Input

Node features of shape ([batch], N, F);
Modified Laplacian of shape ([batch], N, N); can be computed with spektral.utils.convolution.localpooling_filter.

Output

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

layer_appnp(
  object,
  channels,
  alpha = 0.2,
  propagations = 1,
  mlp_hidden = NULL,
  mlp_activation = "relu",
  dropout_rate = 0,
  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
`alpha`	teleport probability during propagation
`propagations`	number of propagation steps
`mlp_hidden`	list of integers, number of hidden units for each hidden layer in the MLP (if None, the MLP has only the output layer)
`mlp_activation`	activation for the MLP layers
`dropout_rate`	dropout rate for Laplacian and MLP layers
`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.