OSINT Graph Analyzer 🕵️

Build knowledge graphs from OSINT data and discover hidden patterns using Neo4j graph algorithms.

What It Does

Ingests OSINT data from multiple sources and creates a Neo4j knowledge graph for:

• Entity linking — Connect same person across platforms
• Community detection — Find clusters of related entities
• Centrality analysis — Identify key influencers in networks
• Path analysis — Trace connections between entities
• Pattern recognition — Detect anomalies and hidden relationships

Use Cases

• Investigation workflows — Map relationships in complex cases
• Threat intelligence — Identify central nodes in attack networks
• Social network analysis — Discover communities and influence patterns
• Counter-OSINT — Understand your own exposure surface

Requirements

• Neo4j 5.x (local or remote)
• Python 3.9+
• neo4j-driver package

Usage

# Start Neo4j instance (local)
docker run -d \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  neo4j:5.23

# Ingest data
python3 scripts/osint-graph.py --ingest data/sources.csv

# Run community detection
python3 scripts/osint-graph.py --community-detection

# Find most central entities
python3 scripts/osint-graph.py --centrality --top 10

# Trace path between two entities
python3 scripts/osint-graph.py --path "Entity A" "Entity B"

# Export graph as visualization
python3 scripts/osint-graph.py --export graph.json

Data Format

Supported formats:

• CSV (node + edge files)
• JSON (Cypher queries)
• Direct API ingestion (Telegram, Twitter, etc.)

CSV example:

nodes.csv:
id,name,type,properties
1,@target_account,person,"{country:US,verified:true}"
2,@associated_handle,person,"{country:RU}"

edges.csv:
source,target,relationship,timestamp
1,2,MENTIONED,2026-01-31

Graph Algorithms

Algorithm	What It Finds	Use Case
Louvain	Community clusters	Find groups working together
PageRank	Influence centrality	Identify key influencers
Betweenness	Bridge nodes	Find connection points between communities
Shortest Path	Connection chains	Trace indirect relationships
Weakly Connected	Disconnected subgraphs	Find isolated clusters

Architecture

┌─────────────────┐
│  Ingestion      │  ← CSV/JSON/API sources
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Neo4j Graph    │  ← Nodes + Relationships
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Algorithms     │  ← GraphX / Neo4j Graph Algorithms
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Visualization  │  ← JSON export + D3.js / Cytoscape
└─────────────────┘

Inspiration

• CRIS — Multi-agent criminal intelligence system with Neo4j
• Context Graphs — Semantic search + structural analysis
• osint-analyser — LLM-powered OSINT automation

Local-Only Promise

• Data stays local (Neo4j instance)
• No external API calls for analysis
• Optional offline mode

Version History

• v0.1 — MVP: CSV ingest, basic algorithms, JSON export
• Roadmap: API integration, ML anomaly detection, real-time updates