Module gnn 

Graph Neural Network layers for learning on graph-structured data.

This module provides neural network layers that operate on graphs, enabling learning from node features and graph topology.

Architecture

Node Features    Graph Structure
     │                 │
     ▼                 ▼
┌────────────────────────────┐
│       GNN Layer            │
│  (aggregate + transform)   │
└────────────────────────────┘
           │
           ▼
   Updated Node Features

Layers

• GCNConv - Graph Convolutional Network (Kipf & Welling, 2017)
• GATConv - Graph Attention Network (Velickovic et al., 2018)

Example

use aprender::gnn::{GCNConv, GNNModule};
use aprender::autograd::Tensor;

// Create GCN layer: 16 input features → 32 output features
let gcn = GCNConv::new(16, 32);

// Node features [num_nodes, in_features]
let x = Tensor::ones(&[4, 16]);

// Adjacency matrix (COO format): edge_index[2, num_edges]
let edge_index = vec![(0, 1), (1, 2), (2, 3), (3, 0)];

let out = gcn.forward_gnn(&x, &edge_index);
assert_eq!(out.shape(), &[4, 32]);

References

• Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.
• Velickovic, P., et al. (2018). Graph Attention Networks. ICLR.
• Hamilton, W. L., et al. (2017). Inductive Representation Learning on Large Graphs (GraphSAGE). NeurIPS.

Structs

EdgeConv

Edge convolution layer (Wang et al., 2019).

GATConv

Graph Attention Network layer (Velickovic et al., 2018).

GCNConv

Graph Convolutional Network layer (Kipf & Welling, 2017).

GINConv

Graph Isomorphism Network layer (Xu et al., 2019).

GraphSAGEConv

GraphSAGE layer (Hamilton et al., 2017).

Enums

SAGEAggregation

Aggregation method for GraphSAGE.

Traits

GNNModule

Trait for GNN modules that process graph-structured data.

Functions

global_max_pool

Global max pooling for graph-level predictions.

global_mean_pool

Global mean pooling for graph-level predictions.

global_sum_pool

Global sum pooling for graph-level predictions.

Type Aliases

EdgeIndex

Edge index type: (source_node, target_node)