Locy Use Case: Fraud Risk Propagation¶

Propagate account risk backward over transfer edges and isolate clean accounts.

This notebook uses schema-first mode (recommended): labels, edge types, and typed properties are defined before ingest.

How To Read This Notebook¶

• Step 1 initializes an isolated local database.
• Step 2 defines schema (the recommended production path).
• Step 3 seeds a minimal graph for this use case.
• Step 4 declares Locy rules and query statements.
• Steps 5-6 evaluate and inspect command/query outputs.
• Step 7 tells you what to look for in the results.

1) Setup¶

Creates a temporary database directory so the example is reproducible and leaves no state behind.

In [1]:

import os
import shutil
import tempfile
from pprint import pprint

import uni_db

DB_DIR = tempfile.mkdtemp(prefix="uni_locy_")
print("DB_DIR:", DB_DIR)

db = uni_db.Database(DB_DIR)

import os import shutil import tempfile from pprint import pprint import uni_db DB_DIR = tempfile.mkdtemp(prefix="uni_locy_") print("DB_DIR:", DB_DIR) db = uni_db.Database(DB_DIR)

DB_DIR: /tmp/uni_locy_qehys8zw

2) Define Schema (Recommended)¶

Define labels, property types, and edge types before inserting data.

In [2]:

(
    db.schema()
    .label("Account")
        .property("id", "string")
        .property("flagged", "bool")
    .done()
    .edge_type("TRANSFER", ["Account"], ["Account"])
    .done()
    .apply()
)

print('Schema created')

( db.schema() .label("Account") .property("id", "string") .property("flagged", "bool") .done() .edge_type("TRANSFER", ["Account"], ["Account"]) .done() .apply() ) print('Schema created')

Schema created

3) Seed Graph Data¶

Insert only the entities/relationships needed for this scenario so rule behavior stays easy to inspect.

In [3]:

db.execute("CREATE (:Account {id: 'A1', flagged: true})")
db.execute("CREATE (:Account {id: 'A2', flagged: false})")
db.execute("CREATE (:Account {id: 'A3', flagged: false})")
db.execute("CREATE (:Account {id: 'A4', flagged: false})")
db.execute("MATCH (a1:Account {id:'A1'}), (a2:Account {id:'A2'}) CREATE (a1)-[:TRANSFER]->(a2)")
db.execute("MATCH (a2:Account {id:'A2'}), (a3:Account {id:'A3'}) CREATE (a2)-[:TRANSFER]->(a3)")
db.execute("MATCH (a4:Account {id:'A4'}), (a3:Account {id:'A3'}) CREATE (a4)-[:TRANSFER]->(a3)")
print('Seeded graph data')

db.execute("CREATE (:Account {id: 'A1', flagged: true})") db.execute("CREATE (:Account {id: 'A2', flagged: false})") db.execute("CREATE (:Account {id: 'A3', flagged: false})") db.execute("CREATE (:Account {id: 'A4', flagged: false})") db.execute("MATCH (a1:Account {id:'A1'}), (a2:Account {id:'A2'}) CREATE (a1)-[:TRANSFER]->(a2)") db.execute("MATCH (a2:Account {id:'A2'}), (a3:Account {id:'A3'}) CREATE (a2)-[:TRANSFER]->(a3)") db.execute("MATCH (a4:Account {id:'A4'}), (a3:Account {id:'A3'}) CREATE (a4)-[:TRANSFER]->(a3)") print('Seeded graph data')

Seeded graph data

4) Locy Program¶

CREATE RULE defines derived relations. QUERY ... WHERE ... RETURN ... reads from those relations.

In [4]:

program = r'''
CREATE RULE risky_seed AS
MATCH (a:Account)
WHERE a.flagged = true
YIELD KEY a

CREATE RULE risky AS
MATCH (a:Account)
WHERE a IS risky_seed
YIELD KEY a

CREATE RULE risky AS
MATCH (a:Account)-[:TRANSFER]->(b:Account)
WHERE b IS risky
YIELD KEY a

CREATE RULE clean AS
MATCH (a:Account)
WHERE a IS NOT risky
YIELD KEY a

QUERY risky WHERE a.id = a.id RETURN a.id AS risky_account
QUERY clean WHERE a.id = a.id RETURN a.id AS clean_account
'''
print(program)

program = r''' CREATE RULE risky_seed AS MATCH (a:Account) WHERE a.flagged = true YIELD KEY a CREATE RULE risky AS MATCH (a:Account) WHERE a IS risky_seed YIELD KEY a CREATE RULE risky AS MATCH (a:Account)-[:TRANSFER]->(b:Account) WHERE b IS risky YIELD KEY a CREATE RULE clean AS MATCH (a:Account) WHERE a IS NOT risky YIELD KEY a QUERY risky WHERE a.id = a.id RETURN a.id AS risky_account QUERY clean WHERE a.id = a.id RETURN a.id AS clean_account ''' print(program)

CREATE RULE risky_seed AS
MATCH (a:Account)
WHERE a.flagged = true
YIELD KEY a

CREATE RULE risky AS
MATCH (a:Account)
WHERE a IS risky_seed
YIELD KEY a

CREATE RULE risky AS
MATCH (a:Account)-[:TRANSFER]->(b:Account)
WHERE b IS risky
YIELD KEY a

CREATE RULE clean AS
MATCH (a:Account)
WHERE a IS NOT risky
YIELD KEY a

QUERY risky WHERE a.id = a.id RETURN a.id AS risky_account
QUERY clean WHERE a.id = a.id RETURN a.id AS clean_account

5) Evaluate Locy Program¶

Run the program, then inspect materialization stats (iterations, strata, and executed queries).

In [5]:

out = db.locy_evaluate(program)

print("Derived relations:", list(out["derived"].keys()))
stats = out["stats"]
print("Iterations:", stats.total_iterations)
print("Strata:", stats.strata_evaluated)
print("Queries executed:", stats.queries_executed)

out = db.locy_evaluate(program) print("Derived relations:", list(out["derived"].keys())) stats = out["stats"] print("Iterations:", stats.total_iterations) print("Strata:", stats.strata_evaluated) print("Queries executed:", stats.queries_executed)

Derived relations: ['clean', 'risky_seed', 'risky']
Iterations: 2
Strata: 3
Queries executed: 14

6) Inspect Command Results¶

Each command result can contain rows; this is the easiest way to verify your rule outputs and query projections.

In [6]:

print("Derived relation snapshots:")
for rel_name, rel_rows in out["derived"].items():
    print(f"\\n{rel_name}: {len(rel_rows)} row(s)")
    pprint(rel_rows)

if out["command_results"]:
    print("\\nCommand results:")
for i, cmd in enumerate(out["command_results"], start=1):
    print(f"\\nCommand #{i}:", cmd.get("type"))
    rows = cmd.get("rows")
    if rows is not None:
        pprint(rows)
if not out["command_results"]:
    print("\\nNo QUERY/EXPLAIN/ABDUCE command outputs in this program.")

print("Derived relation snapshots:") for rel_name, rel_rows in out["derived"].items(): print(f"\\n{rel_name}: {len(rel_rows)} row(s)") pprint(rel_rows) if out["command_results"]: print("\\nCommand results:") for i, cmd in enumerate(out["command_results"], start=1): print(f"\\nCommand #{i}:", cmd.get("type")) rows = cmd.get("rows") if rows is not None: pprint(rows) if not out["command_results"]: print("\\nNo QUERY/EXPLAIN/ABDUCE command outputs in this program.")

Derived relation snapshots:
\nclean: 3 row(s)
[{'a': {'_id': '1', '_labels': ['Account'], 'flagged': False, 'id': 'A2'}},
 {'a': {'_id': '2', '_labels': ['Account'], 'flagged': False, 'id': 'A3'}},
 {'a': {'_id': '3', '_labels': ['Account'], 'flagged': False, 'id': 'A4'}}]
\nrisky_seed: 1 row(s)
[{'a': {'_id': '0', '_labels': ['Account'], 'flagged': True, 'id': 'A1'}}]
\nrisky: 1 row(s)
[{'a': {'_id': '0', '_labels': ['Account'], 'flagged': True, 'id': 'A1'}}]
\nCommand results:
\nCommand #1: query
[{'risky_account': 'A1'}]
\nCommand #2: query
[{'clean_account': 'A2'}, {'clean_account': 'A3'}, {'clean_account': 'A4'}]

7) What To Expect¶

Use these checks to validate output after evaluation:

• A1 is risky by seed; A2 and A4 become risky by backward propagation through TRANSFER.
• A3 should remain in clean because it does not transfer to a risky account.
• Two query result blocks should appear: one for risky, one for clean.

8) Cleanup¶

Delete the temporary database directory created in setup.

In [7]:

shutil.rmtree(DB_DIR, ignore_errors=True)
print("Cleaned up", DB_DIR)

shutil.rmtree(DB_DIR, ignore_errors=True) print("Cleaned up", DB_DIR)

Cleaned up /tmp/uni_locy_qehys8zw