A business graph is a data model representing businesses as interconnected nodes—brands, legal entities, locations, and people—linked by ownership, operation, and affiliation relationships. Graph models capture complex business structures that flat databases miss.
Why Graphs?
Traditional databases store business records as rows in tables:
GTL Services LLC
- Address: 1209 Orange St
- EIN: 12-3456789
- State: DE
This works for simple lookups but fails when relationships matter:
- Who owns this company?
- What other businesses share this address?
- Is this the same entity operating under a different name elsewhere?
Graphs represent relationships as first-class data.
Graph Structure
Nodes (Entities)
Different types of nodes represent different concepts:
- Brand nodes: Customer-facing business identities
- Entity nodes: Legal structures (LLCs, corporations)
- Location nodes: Physical addresses
- Person nodes: Individuals (owners, officers, agents)
Edges (Relationships)
Edges connect nodes with typed relationships:
- owns: Person → Entity, Entity → Entity
- operates_as: Entity → Brand
- located_at: Brand → Location, Entity → Location
- registered_at: Entity → Location
- officer_of: Person → Entity
Example
[Person: Jane Smith]
|
| owns (60%)
↓
[Entity: Smith Holdings LLC]
|
| owns (100%)
↓
[Entity: GTL Services LLC]
|
+-- operates_as → [Brand: Green Thumb Landscaping]
|
+-- registered_at → [Location: 1209 Orange St, Wilmington DE]
|
+-- located_at → [Location: 456 Main St, Columbus OH]
What Graphs Reveal
Ownership Chains
Trace through layers of ownership to find ultimate beneficial owners:
Target Company ← Holding Company ← Trust ← Individual
Hidden Connections
Discover non-obvious relationships:
- Two seemingly unrelated businesses share the same registered agent
- An officer appears across dozens of entities
- A single address hosts hundreds of registrations
Network Patterns
Identify suspicious structures:
- Circular ownership (A owns B owns C owns A)
- Star patterns (one person connected to many entities)
- Clusters of entities created on the same date
Graphs in KYB
Verification
Graphs enable richer verification:
- Connect the brand name on an application to the legal entity in state records
- Verify that stated ownership matches actual corporate structure
- Confirm operating locations are real, not just registered agent addresses
Risk Assessment
Graph patterns signal risk:
- Entity with only registered agent connections → potential shell company
- Person linked to many recently formed entities → possible formation agent
- Unusual ownership depth or complexity → requires EDD
Monitoring
Graphs support ongoing monitoring:
- Alert when ownership changes
- Detect new entity connections
- Track expansion or contraction of business networks
Building Business Graphs
Data Sources
Graphs are constructed from multiple sources:
- Secretary of State filings (entities, officers, agents)
- Business registries (ownership, structure)
- Web data (brands, locations)
- Transaction data (operational presence)
Entity Resolution
Entity resolution is essential—the same business appears differently across sources. Resolution creates the links that make graphs useful.
Continuous Updates
Business structures change. Graphs require:
- Regular data refreshes
- Change detection
- Historical versioning
Key Takeaways
- Graphs model relationships between businesses, people, and locations
- Nodes and edges capture both entities and their connections
- Ownership chains become visible through graph traversal
- Hidden patterns emerge—shared agents, suspicious clusters, complex structures
- Graphs power advanced KYB—richer verification, better risk detection, effective monitoring
Related: Business Identity | Entity Resolution | Beneficial Ownership
