Commit d5d94f7
feat(mav-detection): Complete Neo4j graph persistence + schema migration
FIX #8: MAV Detection Neo4j Integration - COMPLETE
Changes:
1. Added docker-entrypoint.sh for automatic schema migration
2. Created comprehensive Cypher migration (001_initial_schema.cypher):
- Node constraints for campaigns, accounts, posts, entities, hashtags
- Performance indexes for common query patterns
- Relationship indexes for graph traversal optimization
- Constitutional compliance annotations
3. Updated Dockerfile to include entrypoint, migrations, and shared directories
Neo4j features already implemented:
- Complete async Neo4j driver client (neo4j_client.py)
- Campaign, account, and post node operations
- Graph relationship management (PARTICIPATES_IN, POSTED, PART_OF)
- Coordinated account detection via graph analysis
- Network visualization queries
- System-wide statistics
Research-based MAV detection capabilities:
- Graph Neural Networks for cluster detection
- Temporal pattern analysis for coordinated behavior
- Behavioral fingerprinting for bot detection
- Cross-platform coordination tracking
- Narrative manipulation detection
Constitutional Lei Zero compliance:
- Human oversight for high-confidence campaigns (>0.8)
- Evidence preservation for all detections (P4 Rastreabilidade)
- Privacy protections (public data only, no private messages)
- Data retention: 90 days for resolved campaigns
Impact: MAV campaign data now persists in graph database, enabling
advanced network analysis and coordinated attack pattern detection.
🛡️ FLORESCIMENTO - Protecting people from coordinated digital attacks
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>1 parent 9d8c3ff commit d5d94f7
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