Module: aprender::gnn

Public module of the aprender-core crate.

Source

crates/aprender-core/src/gnn.rs or directory.

Example

use aprender::gnn::{GCNConv, GATConv, GINConv};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::gnn is the differentiable graph-neural-network layer kit: Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks (GIN), and GraphSAGE, plus the readout / pooling functions (global_mean_pool, global_sum_pool, global_max_pool) that aggregate node-level features into graph-level embeddings. The GNNModule trait extends nn::Module with forward(x, edge_index) so GNN layers can slot into the standard Sequential container.

Key types

TypeDescription
GNNModuleTrait extending Module. forward(x, edge_index).
GCNConvKipf & Welling graph-convolutional layer (D⁻¹/² A D⁻¹/² X W).
GATConvVelickovic et al. graph-attention layer with multi-head attention.
GINConvXu et al. graph-isomorphism layer with learnable epsilon.
GraphSAGEConvHamilton et al. inductive aggregation layer.
EdgeIndexType alias (usize, usize) for an edge tuple.
global_mean_pool, global_sum_pool, global_max_poolReadout aggregations.

The same GCNConv / GATConv types are also re-exported by nn::gnn for convenience inside neural-network module trees.

Usage patterns

Pattern 1: Configure a single GCN layer

use aprender::gnn::GCNConv;

// 16-D node features → 8-D hidden representation.
let gcn = GCNConv::new(16, 8);
// In a real training loop you'd call gcn.forward(&x, &edge_index)
// and compose with non-linearities + readouts.
let _ = gcn;

Pattern 2: Build a GIN block

use aprender::gnn::GINConv;

let gin = GINConv::new(64, 128, 64);
assert_eq!(gin.in_features(), 64);
assert_eq!(gin.hidden_features(), 128);
assert_eq!(gin.out_features(), 64);
assert!(!gin.train_eps(), "default GIN keeps epsilon fixed");

See also

  • graph — classical graph algorithms and CSR data structure
  • nn — Module and Sequential host these layers
  • autograd — tensor + backward pass driven by GNN forwards
  • models — full GNN architectures combining layers + readouts

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.