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Multi-layer GraphSAGE Model (Hamilton et al. 2017)

model_sage.Rd

Stacks multiple GraphSAGE layers with configurable aggregation functions.

Usage

model_sage(
  in_features,
  hidden_dims,
  out_features,
  aggregator = MeanAggregator(),
  activation = nnf_relu,
  out_activation = NULL,
  dropout = 0,
  concat = 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)

aggregator

Aggregator S7 object. Aggregation function for all layers. Default: MeanAggregator()

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

concat

Logical. If TRUE, concatenates self and neighbor features. If FALSE, adds them. Default: TRUE

x

Tensor n_nodes x in_features. Node feature matrix (dense or sparse)

adj

Sparse torch tensor n_nodes x n_nodes. Adjacency matrix defining graph structure. Must be a sparse COO tensor.

Value

Tensor n_nodes x out_features. Final predictions

Details

Architecture:

  • L hidden SAGE layers with configurable activation

  • 1 output SAGE layer with optional output activation

  • Total layers = length(hidden_dims) + 1

Each layer aggregates neighbor features using the specified aggregator, then combines with self features via concatenation or addition.

Forward pass

References

Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30. doi:10.48550/arXiv.1706.02216

Examples

if (FALSE) { # \dontrun{
# Binary classification with sigmoid and mean aggregation
model <- model_sage(14, c(56, 56), 1, output_activation = nnf_sigmoid)

# Multi-class with softmax and max aggregation
model <- model_sage(
  14,
  c(32, 32),
  10,
  aggregator = MaxAggregator(),
  output_activation = function(x) nnf_softmax(x, dim = -1)
)

# Regression with sum aggregation
model <- model_sage(14, c(64, 64), 1, aggregator = SumAggregator())

# With dropout and custom activation
model <- model_sage(
  14,
  c(56, 56),
  1,
  activation = nnf_tanh,
  dropout = 0.5
)
} # }