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Multi-layer GCN Model

Source: R/model-gcn.R
model_gcn.Rd

Stacks multiple GCN layers to create a deep graph convolutional network.

Usage

model_gcn(
  in_features,
  hidden_dims,
  out_features,
  activation = nnf_relu,
  out_activation = NULL,
  dropout = 0,
  normalize = TRUE
)

Arguments

in_features

Integer. Number of input features per node

hidden_dims

Integer vector. Dimensions of hidden layers (length = L)

out_features

Integer. Number of output features (typically 1 for regression)

activation

Function. Activation for hidden layers. Default: nnf_relu

out_activation

Function or NULL. Activation for output layer. Default: NULL

dropout

Numeric. Dropout rate (0-1) applied after each hidden layer. Default: 0

normalize

Logical. Whether to add self-loops and apply symmetric normalization. Default: TRUE

Value

Tensor n_nodes x out_features. Final predictions

Details

Architecture:

  • L hidden GCN layers with configurable activation

  • 1 output GCN layer with optional output activation

  • Total layers = length(hidden_dims) + 1

For example, hidden_dims = c(56, 56) creates:

  • Layer 1: in_features → 56 (activation)

  • Layer 2: 56 → 56 (activation)

  • Layer 3: 56 → out_features (output_activation)

Uses gcn_conv_layer which automatically handles adding self-loops and symmetric normalization when normalize = TRUE.

Forward pass

model(x, adj)

  • x: Tensor n_nodes x in_features. Node feature matrix.

  • adj: Sparse COO tensor n_nodes x n_nodes. Adjacency matrix defining graph structure. When normalize = TRUE, self-loops are added and symmetric normalization is applied by each layer.

Examples

if (FALSE) { # torch::torch_is_installed()
adj <- adj_from_edgelist(from = c(1, 2, 3, 4), to = c(2, 3, 4, 1))
x <- torch::torch_randn(4, 14)

# Regression (no output activation)
model <- model_gcn(14, c(64, 64), 1)
model(x, adj)

# Binary classification with sigmoid
model <- model_gcn(14, c(56, 56), 1, out_activation = torch::nnf_sigmoid)
model(x, adj)

# Multi-class with softmax
model <- model_gcn(
  14,
  c(32, 32),
  10,
  out_activation = function(x) torch::nnf_softmax(x, dim = -1)
)
model(x, adj)

# With dropout and tanh activation
model_gcn(14, c(56, 56), 1, activation = torch::torch_tanh, dropout = 0.5)
}