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

ruvector_gcn_forward(embeddings, src, dst, weights, out_dim) -> FLOAT[][]

2. GNN Aggregation

ruvector_gnn_aggregate(messages, method) -> FLOAT[]

3. GraphSAGE Forward Pass

ruvector_graphsage_forward(embeddings, src, dst, out_dim, num_samples) -> FLOAT[][]

4. Multi-Hop Message Passing

ruvector_message_pass(node_table, edge_table, embedding_col, hops, layer_type) -> TEXT

5. Batch Processing

ruvector_gnn_batch_forward(embeddings_batch, edge_indices, graph_sizes, layer_type, out_dim) -> FLOAT[][]

Usage Examples

Basic GCN

SELECT ruvector_gcn_forward(
    ARRAY[ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]],
    ARRAY[0], ARRAY[1], NULL, 8
);

Aggregation

SELECT ruvector_gnn_aggregate(
    ARRAY[ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]],
    'mean'
);

GraphSAGE with Sampling

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:

// 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:

   CREATE EXTENSION ruvector;
   

2. Check Functions:

   \df ruvector_gnn_*
   \df ruvector_gcn_*
   \df ruvector_graphsage_*
   

3. Run Examples: See GNN_USAGE_EXAMPLES.md

References

• Implementation Summary - Architecture details
• Quick Reference - Function reference
• Usage Examples - Real-world applications
• Integration Plan - Original specification

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