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

\loadmathjax

A Graph Isomorphism Network (GIN) as presented by Xu et al. (2018).

Mode: single, disjoint.

This layer expects a sparse adjacency matrix.

This layer computes for each node \mjeqni: \mjdeqn Z_i = \textrmMLP\big( (1 + \epsilon) \cdot X_i + \sum\limits_j \in \mathcalN(i) X_j \big) where \mjeqn\textrmMLP is a multi-layer perceptron.

Input

Node features of shape (N, F);
Binary adjacency matrix of shape (N, N).

Output

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

layer_gin_conv(
  object,
  channels,
  epsilon = NULL,
  mlp_hidden = NULL,
  mlp_activation = "relu",
  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`	integer, number of output channels
`epsilon`	unnamed parameter, see Xu et al. (2018), and the equation above. By setting `epsilon=None`, the parameter will be learned (default behaviour). If given as a value, the parameter will stay fixed.
`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
`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.