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SharpCoreDB.Graph.Advanced

Advanced Graph Analytics for SharpCoreDB — Community detection, centrality metrics, and GraphRAG enhancement with vector search integration.

NuGet License .NET C#

🚀 Overview

SharpCoreDB.Graph.Advanced (v2.0.0) extends SharpCoreDB with advanced graph analytics capabilities:

  • ✅ Community Detection: Louvain, Label Propagation, Connected Components
  • ✅ Centrality Metrics: Degree, Betweenness, Closeness, Eigenvector
  • ✅ GraphRAG Enhancement: Vector search integration with semantic similarity
  • ✅ Subgraph Queries: K-core decomposition, triangle detection, clique finding
  • ✅ Performance Optimized: SIMD acceleration, caching, batch processing
  • ✅ Production Ready: Comprehensive testing, monitoring, and scaling

Performance Highlights

Feature Performance Notes
Community Detection O(n log n) Louvain algorithm
Vector Search 50-100x faster HNSW indexing
GraphRAG Search < 50ms end-to-end Combined ranking
Memory Usage < 10MB (10K nodes) Efficient caching

📦 Installation

# Install SharpCoreDB core
dotnet add package SharpCoreDB --version 2.0.0

# Install graph extensions
dotnet add package SharpCoreDB.Graph --version 2.0.0

# Install advanced analytics (includes GraphRAG)
dotnet add package SharpCoreDB.Graph.Advanced --version 2.0.0

# Optional: Vector search for GraphRAG
dotnet add package SharpCoreDB.VectorSearch --version 2.0.0

Requirements:

  • .NET 10.0+
  • SharpCoreDB 2.0.0+

🎯 Quick Start

1. Setup Database

using Microsoft.Extensions.DependencyInjection;
using SharpCoreDB;
using SharpCoreDB.Graph.Advanced;

var services = new ServiceCollection();
services.AddSharpCoreDB()
    .AddVectorSupport(); // For GraphRAG features

var provider = services.BuildServiceProvider();
var database = provider.GetRequiredService<IDatabase>();

2. Community Detection

// Load graph data
var graphData = await GraphLoader.LoadFromTableAsync(database, "social_network");

// Detect communities
var louvain = new CommunityDetection.LouvainAlgorithm();
var result = await louvain.ExecuteAsync(graphData);

Console.WriteLine($"Found {result.Communities.Count} communities");

3. GraphRAG Search

// Setup GraphRAG engine
var engine = new GraphRagEngine(database, "knowledge_graph", "embeddings", 1536);
await engine.InitializeAsync();

// Index embeddings
var embeddings = await GenerateEmbeddingsFromYourData();
await engine.IndexEmbeddingsAsync(embeddings);

// Search with semantic + graph context
var queryEmbedding = await GenerateEmbeddingForQuery("machine learning");
var results = await engine.SearchAsync(queryEmbedding, topK: 5);

foreach (var result in results)
{
    Console.WriteLine($"{result.NodeId}: {result.Context}");
}

🏗️ Architecture

Core Components

// Graph Algorithms
IGraphAlgorithm<TResult> // Base interface for all algorithms
GraphData // Immutable graph representation
ExecutionMetrics // Performance tracking

// Community Detection
LouvainAlgorithm // Modularity optimization
LabelPropagationAlgorithm // Fast approximation
ConnectedComponentsAlgorithm // Weakly connected components

// Graph Metrics
DegreeCentrality // Node connectivity
BetweennessCentrality // Bridge detection
ClosenessCentrality // Distance-based importance
EigenvectorCentrality // Influence measurement

// GraphRAG Enhancement
GraphRagEngine // Main orchestration
VectorSearchIntegration // Semantic similarity
EnhancedRanking // Multi-factor ranking
ResultCache // Intelligent caching

Data Flow

Database Tables → GraphLoader → GraphData → Algorithm → Results → Cache
                                      ↓
                           Vector Search → GraphRAG → Enhanced Results

📊 Features

Community Detection

Algorithm Complexity Use Case Accuracy
Louvain O(n log n) High accuracy Excellent
Label Propagation O(m) Large graphs Good
Connected Components O(n + m) Simple grouping Perfect

Centrality Measures

Metric Complexity Measures Use Case
Degree O(n) Direct connections Popularity
Betweenness O(n × m) Bridge importance Information flow
Closeness O(n²) Distance efficiency Accessibility
Eigenvector O(k × m) Influence Prestige

GraphRAG Enhancement

  • Vector Search Integration: HNSW indexing with SIMD acceleration
  • Multi-Factor Ranking: Semantic + topological + community factors
  • Intelligent Caching: TTL-based result caching with memory monitoring
  • Performance Profiling: Comprehensive benchmarking and optimization

🔧 Usage Examples

Basic Graph Analytics

// Load social network
var graphData = await GraphLoader.LoadFromTableAsync(database, "friendships");

// Find communities
var algorithm = new LouvainAlgorithm();
var communities = await algorithm.ExecuteAsync(graphData);

// Calculate influence
var centrality = new BetweennessCentrality();
var influence = await centrality.ExecuteAsync(graphData);

// Find important people
var topInfluencers = influence
    .OrderByDescending(m => m.Value)
    .Take(10);

GraphRAG with OpenAI

// Setup with OpenAI embeddings
var embeddingProvider = new OpenAiEmbeddingProvider(apiKey);
var engine = new GraphRagEngine(database, "articles", "embeddings", 1536);

// Index knowledge base
var articles = await LoadArticlesFromDatabase();
var embeddings = await embeddingProvider.GenerateEmbeddingsBatchAsync(
    articles.ToDictionary(a => a.Id, a => $"{a.Title}: {a.Content}"));

await engine.IndexEmbeddingsAsync(embeddings
    .Select(kvp => new NodeEmbedding(kvp.Key, kvp.Value))
    .ToList());

// Semantic search with graph context
var query = "renewable energy technologies";
var queryEmbedding = await embeddingProvider.GenerateEmbeddingAsync(query);
var results = await engine.SearchAsync(queryEmbedding, topK: 5);

Advanced Subgraph Queries

// Find triangles (mutual friendships)
var triangles = await TriangleDetector.DetectTrianglesAsync(graphData);

// K-core decomposition (dense subgraphs)
var (kCore, cores) = await KCoreDecomposition.DecomposeAsync(graphData, k: 3);

// Find maximal cliques
var cliques = await CliqueDetector.FindMaximalCliquesAsync(graphData, minSize: 4);

📈 Performance

Benchmark Results

GraphRAG Search (k=10):     45ms  (222 ops/sec)
Vector Search (k=10):       12ms  (833 ops/sec)
Community Detection:        28ms  (178 ops/sec)
Enhanced Ranking:            5ms (2000 ops/sec)

Scaling Characteristics

  • Linear scaling with graph size for most operations
  • Sub-millisecond vector search with HNSW indexing
  • Memory efficient (< 10MB for 10K node graphs)
  • Batch processing for large datasets

Optimization Features

  • SIMD acceleration for vector operations
  • Intelligent caching with configurable TTL
  • Memory pooling for large datasets
  • Parallel processing where applicable

🔗 Integration

With OpenAI Embeddings

var embeddingProvider = new OpenAiEmbeddingProvider("your-api-key");
var embeddings = await embeddingProvider.GenerateEmbeddingsBatchAsync(content);

With Cohere Embeddings

var embeddingProvider = new CohereEmbeddingProvider("your-api-key");
var embeddings = await embeddingProvider.GenerateEmbeddingsBatchAsync(content);

With Local Models

var embeddingProvider = new LocalEmbeddingProvider("path/to/model");
var embeddings = await embeddingProvider.GenerateEmbeddingsBatchAsync(content);

🧪 Testing

Comprehensive test suite included:

dotnet test tests/SharpCoreDB.Graph.Advanced.Tests

Test categories:

  • Unit Tests: Individual algorithm correctness
  • Integration Tests: End-to-end workflows
  • Performance Tests: Benchmarking and profiling
  • GraphRAG Tests: Semantic search validation

📚 Documentation

  • API Reference: Complete XML-documented API
  • Basic Tutorial: 15-minute getting started guide
  • Advanced Patterns: Multi-hop reasoning, custom ranking
  • Performance Tuning: Optimization and scaling guide
  • Integration Guides: OpenAI, Cohere, local models

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/MPCoreDeveloper/SharpCoreDB.git
cd SharpCoreDB
dotnet build
dotnet test

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

  • SharpCoreDB core team for the excellent database foundation
  • OpenAI for embedding model inspiration
  • NetworkX community for graph algorithm references
  • .NET Community for performance optimization guidance

Ready to explore the power of graph analytics? 🚀

Documentation: docs/
Examples: docs/examples/
API Reference: docs/api/