Beginner Level
What Is It?
Knowledge graphs are structured networks of entities (assets, events, indicators) and relationships that represent financial concepts and their causal or temporal connections.
Origin
Popularized by Google in 2012; applied to finance for entity resolution and systemic risk mapping in the late 2010s.
Why It Matters
They convert unstructured market data into a queryable, explainable intelligence layer.
Intermediate Level
Market Mechanics
Nodes represent companies, macroeconomic variables, or events. Edges capture causal, correlation, or temporal relationships with confidence weights.
How It Behaves
Graphs enable complex multi-hop queries and propagate information across related market concepts in real time.
Key Data to Watch
- Graph density and centrality metrics
- Relationship confidence scores
- Propagation speed of new information
Advanced Level
Institutional Behavior
Used by macro funds and risk departments for systemic risk mapping and cross-asset dependency tracking.
Professional Use Cases
- Supply-chain shock propagation analysis
- Corporate ownership and influence mapping
- Macro regime causal inference
AI Interpretation in Systems Like Arkhe
- Supervisor Agent: Maintains and updates the master Arkhe knowledge graph.
- Macro Agent: Queries causal links for regime detection and scenario analysis.
Key Takeaways
Knowledge graphs provide the connective tissue that turns isolated data into true market intelligence.