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TuringDB Example Notebooks

This collection of notebooks demonstrates how to use TuringDB for real-world analytical examples. Each notebook focuses on a different domain and use case, from fraud detection to biological graph exploration, leveraging the performance and versioning capabilities of TuringDB.

Explore the full set of notebooks on GitHub:

github.com/turing-db/turingdb-examples

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Domain: Finance & Fraud Detection

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Paysim Financial Fraud Detection

Uses the synthetic PaySim dataset to simulate a transactional graph. Highlights how graph queries can uncover suspicious transaction patterns and how TuringDB supports fast multi-hop detection for fraud rings and mule accounts.

Query examples:

// Find all transactions between two accounts
MATCH (a1:Account)-->(t:Transaction)-->(a2:Account)
RETURN a1.id, a1.type, t.amount, t.type, a2.id, a2.type, t.is_fraud

// Find all 2-hops transaction chains
MATCH (a1:Account)-[s1:SENT]->(t1:Transaction)-[r1:RECEIVED]->
      (a2:Account)-[s2:SENT]->(t2:Transaction)-[r2:RECEIVED]->
      (a3:Account)
RETURN a1.id, a2.id, a3.id, t1.id, t1.type, t1.amount, t1.step, t1.is_fraud ,
       t2.id, t2.type, t2.amount, t2.step, t2.is_fraud 

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Crypto Orbitaal Fraud Detection

Demonstrates risk analysis on crypto transaction data. Focuses on exploring wallets, exchanges, and fund flows to detect potential illicit activity through graph-based features such as suspicious loops and layered structures.

Query examples:

// Find all 4-hops transaction chains
MATCH (a:Entity)-[:TRANSACTION]->(b:Entity)-[:TRANSACTION]->
      (c:Entity)-[:TRANSACTION]->(d:Entity)
RETURN a, b, c, d

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Domain: Transport & Logistics

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London Transport (TfL)

Models the London transport network as a graph. Investigates connections between stations, line transfers, and potential applications for routing or infrastructure planning using fast traversal queries.

Query examples:

// Check if there is a path between
// Notting Hill Gate and High Street Kensington stations
// in 1 hop specifically using Circle Line
MATCH (start)-[e:CONNECTED]->(end)
WHERE start.displayName = 'Notting Hill Gate'
AND e.line = 'Circle'
AND end.displayName = 'High Street Kensington'
RETURN start.displayName, e.Line, end.displayName

// Find 8-hops paths between Paddington and Blackfriars stations
MATCH (start:Station)-[e1:CONNECTED]->
      (s1:Station)-[e2:CONNECTED]->
      (s2:Station)-[e3:CONNECTED]->
      (s3:Station)-[e4:CONNECTED]->
      (s4:Station)-[e5:CONNECTED]->
      (s5:Station)-[e6:CONNECTED]->
      (s6:Station)-[e7:CONNECTED]->
      (s7:Station)-[e8:CONNECTED]->
      (end:Station)
WHERE start.displayName = 'Paddington'
AND end.displayName = 'Blackfriars'
RETURN start.displayName, start.Note, e1.Line,
       s1.displayName, s1.Note, e2.Line,
       s2.displayName, s2.Note, e3.Line, 
       s3.displayName, s3.Note, e4.Line, 
       s4.displayName, s4.Note, e5.Line, 
       s5.displayName, s5.Note, e6.Line, 
       s6.displayName, s6.Note, e7.Line, 
       s7.displayName, s7.Note, e8.Line, 
       end.displayName, end.Note

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Supply Chain — ETO Chip Explorer

Illustrates how to model a manufacturing and supply chain graph. Tracks the flow of materials (e.g. ETO chips) through suppliers, batches, and final assemblies — enabling traceability, root cause analysis, and risk assessment.

Query examples:

// Find all relationships involving FPGAs
MATCH (n)-[e]->(m)
WHERE n.description = 'FPGA'
RETURN n.displayName, n.description, e, m.displayName, m.description

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Domain: Healthcare & Life Sciences

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Reactome Biological Pathways

Explores the Reactome knowledge base of biological pathways. Showcases how TuringDB can rapidly answer multi-hop queries on complex biological interactions such as gene regulation or protein-protein interaction cascades.

Query examples:

// Find all nodes of type "EntityWithAccessionedSequence"
// and referenceType "ReferenceGeneProduct"
MATCH (n:EntityWithAccessionedSequence)
WHERE n.referenceType = 'ReferenceGeneProduct'
RETURN n.displayName, n.schemaClass, n.referenceType

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Healthcare Knowledge Graph

Builds a patient-centric knowledge graph combining symptoms, diagnoses, treatments, and side effects. Demonstrates entity extraction, GML ingestion, and how TuringDB supports explainable AI on structured health data.

Query examples:

// Find all patients who took Paracetamol
MATCH (p:Patient)-[:TOOK_MEDICATION]->
      (m:Medication)
WHERE m.displayName = 'Paracetamol'
RETURN p.id, p.displayName, m.displayName

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CiteAb Antibody Data

Explores the CiteAb antibody database as a knowledge graph linking antibodies, publications, and suppliers. Demonstrates property-based filtering, edge traversal, and LLM-assisted natural language querying with an auto-generated Cypher system prompt.

Query examples:

// Top publications by citation count
MATCH (n:Publication)
RETURN n.displayName, n.cited_by_count
ORDER BY n.cited_by_count DESC
LIMIT 10

// Publications from a specific year using toInteger()
MATCH (n:Publication)
WHERE n.year = toInteger("2022")
RETURN n.displayName, n.year
LIMIT 10

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Domain: Corporate & Governance

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French Corporate Network

Builds a corporate governance graph from the French public company registry (CAC 40 companies). Models board members, executive roles, and audit firms. Explores interlocking directorates (people sitting on multiple boards), non-executive roles, and media-corporate board overlaps through graph queries and interactive PyVis visualizations.

Query examples:

// All executive officers of a given company
MATCH (p:Person)-[r:IS_OFFICER_OF]->(c:Company)
WHERE c.displayName = 'Air Liquide'
RETURN p.displayName, r.qualite

// All Directeur Général across CAC 40 companies
MATCH (p:Person)-[r:IS_OFFICER_OF]->(c:Company)
WHERE r.qualite = 'Directeur Général'
RETURN p.displayName, c.displayName
ORDER BY c.displayName

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Integrate RDF File

Shows how to convert an RDF/OWL ontology file into TuringDB’s JSONL format and load it as a graph. Covers subject–predicate–object triple flattening, label mapping, and querying the resulting semantic graph.

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Running the Notebooks

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Quick Start

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Prerequisites

• Python 3.13 or higher
• uv package manager

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Installation

1. Clone the repository:

   git clone https://github.com/turing-db/turingdb-examples.git
   cd turingdb-examples

2. Install dependencies:

   uv sync

3. Start Jupyter Lab:

   uv run jupyter lab

   This will start the Jupyter server and display output like:

   [I 2024-09-26 16:30:15.123 ServerApp] Jupyter Server is running at:
   [I 2024-09-26 16:30:15.123 ServerApp] http://localhost:8888/lab?token=abc123...
   

   Copy the URL (starting with http://localhost:8888/lab?token=...) and paste it into your web browser to access Jupyter Lab.

4. Open and run the example notebooks in examples/notebooks/public_version/