semantica/docs/cookbook/advanced/Advanced_Graph_Analytics.ipynb

Advanced Graph Analytics

Overview

This notebook demonstrates advanced graph analytics using GraphAnalyzer, CentralityCalculator, CommunityDetector, ConnectivityAnalyzer, GraphValidator, and Deduplicator.

Learning Objectives

• Use GraphAnalyzer for comprehensive graph analysis
• Use CentralityCalculator for advanced centrality measures
• Use CommunityDetector for community detection
• Use ConnectivityAnalyzer for connectivity analysis
• Use GraphValidator and Deduplicator for graph quality

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Workflow: Graph Analysis → Centrality → Communities → Connectivity → Validation → Deduplication

In [ ]:

from semantica.kg import GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector, ConnectivityAnalyzer, GraphValidator, Deduplicator

builder = GraphBuilder()
analyzer = GraphAnalyzer()

entities = [
    {"id": "e1", "type": "Organization", "name": "Apple Inc.", "properties": {}},
    {"id": "e2", "type": "Person", "name": "Tim Cook", "properties": {}},
    {"id": "e3", "type": "Location", "name": "Cupertino", "properties": {}}
]

relationships = [
    {"source": "e2", "target": "e1", "type": "CEO_of", "properties": {}},
    {"source": "e1", "target": "e3", "type": "located_in", "properties": {}}
]

kg = builder.build(entities, relationships)

metrics = analyzer.compute_metrics(kg)

print(f"Graph metrics:")
print(f"  Entities: {metrics.get('entity_count', 0)}")
print(f"  Relationships: {metrics.get('relationship_count', 0)}")
print(f"  Density: {metrics.get('density', 0):.3f}")

Step 2: Advanced Centrality Measures

Calculate multiple centrality measures.

In [ ]:

centrality_calculator = CentralityCalculator()

degree_centrality = centrality_calculator.calculate_centrality(kg, measure="degree")
betweenness_centrality = centrality_calculator.calculate_centrality(kg, measure="betweenness")

print(f"Degree centrality: {len(degree_centrality)} entities")
print(f"Betweenness centrality: {len(betweenness_centrality)} entities")

Step 3: Community Detection

Detect communities in the graph.

In [ ]:

community_detector = CommunityDetector()

communities = community_detector.detect_communities(kg)

print(f"Detected {len(communities)} communities")
for i, community in enumerate(communities[:3], 1):
    print(f"  Community {i}: {len(community)} entities")

Step 4: Connectivity Analysis

Analyze graph connectivity.

In [ ]:

connectivity_analyzer = ConnectivityAnalyzer()

connectivity = connectivity_analyzer.analyze_connectivity(kg)

print(f"Connectivity analysis:")
print(f"  Is connected: {connectivity.get('is_connected', False)}")
print(f"  Components: {len(connectivity.get('components', []))}")

Step 5: Graph Validation and Deduplication

Validate and deduplicate the graph.

In [ ]:

graph_validator = GraphValidator()
deduplicator = Deduplicator()

validation_result = graph_validator.validate(kg)
deduplicated_kg = deduplicator.deduplicate(kg)

print(f"Graph validation: {validation_result.get('valid', False)}")
print(f"Deduplicated entities: {len(deduplicated_kg.get('entities', []))}")

Summary

You've learned advanced graph analytics:

• GraphAnalyzer: Comprehensive graph analysis and metrics
• CentralityCalculator: Multiple centrality measures
• CommunityDetector: Community detection
• ConnectivityAnalyzer: Connectivity analysis
• GraphValidator: Graph validation
• Deduplicator: Graph deduplication