wifi-densepose/crates/ruvector-postgres/docs/GNN_INDEX.md

# GNN Module Index

## Overview

Complete Graph Neural Network (GNN) implementation for ruvector-postgres PostgreSQL extension.

**Total Lines of Code**: 1,301  
**Total Documentation**: 1,156 lines  
**Implementation Status**: ✅ Complete

## Source Files

### Core Implementation (src/gnn/)

| File | Lines | Description |
|------|-------|-------------|
| **mod.rs** | 30 | Module exports and organization |
| **message_passing.rs** | 233 | Message passing framework, adjacency lists, propagation |
| **aggregators.rs** | 197 | Sum/mean/max aggregation functions |
| **gcn.rs** | 227 | Graph Convolutional Network layer |
| **graphsage.rs** | 300 | GraphSAGE with neighbor sampling |
| **operators.rs** | 314 | PostgreSQL operator functions |
| **Total** | **1,301** | Complete GNN implementation |

## Documentation Files

### User Documentation (docs/)

| File | Lines | Purpose |
|------|-------|---------|
| **GNN_IMPLEMENTATION_SUMMARY.md** | 280 | Architecture overview and design decisions |
| **GNN_QUICK_REFERENCE.md** | 368 | SQL function reference and common patterns |
| **GNN_USAGE_EXAMPLES.md** | 508 | Real-world examples and applications |
| **Total** | **1,156** | Comprehensive documentation |

## Key Features

### Implemented Components

✅ **Message Passing Framework**
- Generic MessagePassing trait
- build_adjacency_list() for graph structure
- propagate() for message passing
- propagate_weighted() for edge weights
- Parallel node processing with Rayon

✅ **Aggregation Functions**
- Sum aggregation
- Mean aggregation
- Max aggregation (element-wise)
- Weighted aggregation
- Generic aggregate() function

✅ **GCN Layer**
- Xavier/Glorot weight initialization
- Degree normalization
- Linear transformation
- ReLU activation
- Optional bias terms
- Edge weight support

✅ **GraphSAGE Layer**
- Uniform neighbor sampling
- Multiple aggregator types (Mean, MaxPool, LSTM)
- Separate neighbor/self weight matrices
- L2 normalization
- Inductive learning support

✅ **PostgreSQL Operators**
- ruvector_gcn_forward()
- ruvector_gnn_aggregate()
- ruvector_message_pass()
- ruvector_graphsage_forward()
- ruvector_gnn_batch_forward()

## Testing Coverage

### Unit Tests
- ✅ Message passing correctness
- ✅ All aggregation methods
- ✅ GCN layer forward pass
- ✅ GraphSAGE sampling
- ✅ Edge cases (disconnected nodes, empty graphs)

### PostgreSQL Tests (#[pg_test])
- ✅ SQL function correctness
- ✅ Empty input handling
- ✅ Weighted edges
- ✅ Batch processing
- ✅ Different aggregation methods

## SQL Functions Reference

### 1. GCN Forward Pass
```sql
ruvector_gcn_forward(embeddings, src, dst, weights, out_dim) -> FLOAT[][]
```

### 2. GNN Aggregation
```sql
ruvector_gnn_aggregate(messages, method) -> FLOAT[]
```

### 3. GraphSAGE Forward Pass
```sql
ruvector_graphsage_forward(embeddings, src, dst, out_dim, num_samples) -> FLOAT[][]
```

### 4. Multi-Hop Message Passing
```sql
ruvector_message_pass(node_table, edge_table, embedding_col, hops, layer_type) -> TEXT
```

### 5. Batch Processing
```sql
ruvector_gnn_batch_forward(embeddings_batch, edge_indices, graph_sizes, layer_type, out_dim) -> FLOAT[][]
```

## Usage Examples

### Basic GCN
```sql
SELECT ruvector_gcn_forward(
    ARRAY[ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]],
    ARRAY[0], ARRAY[1], NULL, 8
);
```

### Aggregation
```sql
SELECT ruvector_gnn_aggregate(
    ARRAY[ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]],
    'mean'
);
```

### GraphSAGE with Sampling
```sql
SELECT ruvector_graphsage_forward(
    node_embeddings, edge_src, edge_dst, 64, 10
);
```

## Performance Characteristics

- **Parallel Processing**: All nodes processed concurrently via Rayon
- **Memory Efficient**: HashMap-based adjacency lists for sparse graphs
- **Scalable Sampling**: GraphSAGE samples k neighbors instead of processing all
- **Batch Support**: Process multiple graphs simultaneously
- **Zero-Copy**: Minimal data copying during operations

## Integration

The GNN module is integrated into the main extension via:

```rust
// src/lib.rs
pub mod gnn;
```

All functions are automatically registered with PostgreSQL via pgrx macros.

## Dependencies

- `pgrx` - PostgreSQL extension framework
- `rayon` - Parallel processing
- `rand` - Random neighbor sampling
- `serde_json` - JSON serialization

## Documentation Structure

```
docs/
├── GNN_INDEX.md                    # This file - index of all GNN files
├── GNN_IMPLEMENTATION_SUMMARY.md   # Architecture and design
├── GNN_QUICK_REFERENCE.md          # SQL function reference
└── GNN_USAGE_EXAMPLES.md           # Real-world examples
```

## Source Code Structure

```
src/gnn/
├── mod.rs                # Module exports
├── message_passing.rs    # Core framework
├── aggregators.rs        # Aggregation functions
├── gcn.rs               # GCN layer
├── graphsage.rs         # GraphSAGE layer
└── operators.rs         # PostgreSQL functions
```

## Next Steps

To use the GNN module:

1. **Install Extension**:
   ```sql
   CREATE EXTENSION ruvector;
   ```

2. **Check Functions**:
   ```sql
   \df ruvector_gnn_*
   \df ruvector_gcn_*
   \df ruvector_graphsage_*
   ```

3. **Run Examples**:
   See [GNN_USAGE_EXAMPLES.md](./GNN_USAGE_EXAMPLES.md)

## References

- [Implementation Summary](./GNN_IMPLEMENTATION_SUMMARY.md) - Architecture details
- [Quick Reference](./GNN_QUICK_REFERENCE.md) - Function reference
- [Usage Examples](./GNN_USAGE_EXAMPLES.md) - Real-world applications
- [Integration Plan](../integration-plans/03-gnn-layers.md) - Original specification

---

**Status**: ✅ Implementation Complete  
**Last Updated**: 2025-12-02  
**Version**: 1.0.0