Message Passing Aggregators

Aggregators combine neighbor node features in graph neural networks. Each aggregator implements a different reduction operation (sum, mean, max, etc.) to aggregate features from neighboring nodes.

Usage

Aggregator(name = character(0), learnable = logical(0))

SumAggregator()

MeanAggregator()

MaxAggregator()

MinAggregator()

ProductAggregator()

VarAggregator()

StdAggregator()

LSTMAggregator(in_features, hidden_features = NULL)

SoftmaxAggregator(in_features, learn = TRUE)

Arguments

adj

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

tensor

Torch tensor n_nodes x n_features. Node feature matrix. Can be dense or sparse.

...

Additional arguments passed to specific aggregator methods.

Details

Available aggregators:

• SumAggregator(): Sum of neighbor features

• MeanAggregator(): Mean of neighbor features (with row normalization)

• MaxAggregator(): Element-wise maximum of neighbor features

• MinAggregator(): Element-wise minimum of neighbor features

• ProductAggregator(): Element-wise product of neighbor features

• VarAggregator(): Variance of neighbor features

• StdAggregator(): Standard deviation of neighbor features

• LSTMAggregator(): Not-imlemented

• SoftmaxAggregator(): Not-imlemented