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Multi-layer Graph Isomorphism Network Model (Xu et al. 2019)

model_gin.Rd

Stacks multiple GIN layers with MLP transformations.

Usage

model_gin(
  in_features,
  hidden_dims,
  out_features,
  eps = 0,
  learn_eps = FALSE,
  dropout = 0.5,
  out_activation = NULL
)

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)

eps

Numeric. Initial value for epsilon. Default: 0

learn_eps

Logical. Whether to learn epsilon parameters. Default: FALSE

dropout

Numeric. Dropout rate (0-1) applied between layers. Default: 0.5

out_activation

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

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 GIN layers with MLP transformations

  • 1 output GIN layer with optional output activation

  • Total layers = length(hidden_dims) + 1

Each layer aggregates neighbor features via summation, adds weighted self features, and applies a 2-layer MLP transformation.

Forward pass

References

Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How Powerful are Graph Neural Networks? International Conference on Learning Representations. doi:10.48550/arXiv.1810.00826

Examples

if (FALSE) { # \dontrun{
# Binary classification
model <- model_gin(14, c(64, 64), 1, output_activation = nnf_sigmoid)

# Multi-class classification
model <- model_gin(
  14,
  c(64, 64),
  3,
  output_activation = function(x) nnf_softmax(x, dim = -1)
)

# With learnable epsilon
model <- model_gin(14, c(128), 1, learn_eps = TRUE)
} # }