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Generative AI > Graph RAG > HR Analytics with Graph - Turnover Patterns and Team Connectivity

HR Analytics with Graph - Turnover Patterns and Team Connectivity

Author: Venkata Sudhakar

ShopMax India's HR analytics team wants to identify employees at risk of attrition before they resign, find team bottlenecks where a single person is the bridge between groups, and understand which teams are well-connected versus siloed. Traditional SQL reports on headcount and tenure miss the structural patterns in who works with whom. Neo4j stores collaboration data as a graph of COLLABORATED_ON relationships and lets the HR team run connectivity queries to surface hidden risks and strengths in the organization.

Degree centrality measures how many direct connections an employee has - high centrality means a collaboration hub. Betweenness centrality measures how often an employee appears on the shortest path between other employees - high betweenness means a structural bridge whose departure would fragment the network. Isolated employees (degree zero or one) are at higher attrition risk. These metrics can be computed with simple Cypher degree() calls or through the Neo4j Graph Data Science (GDS) library for large graphs.

The example below creates a ShopMax India collaboration graph where employees have COLLABORATED_ON relationships weighted by the number of joint projects. It then identifies the top collaborators by degree, flags isolated employees, and finds employees who are the sole link between two groups.

from neo4j import GraphDatabase

URI = "bolt://localhost:7687"
AUTH = ("neo4j", "password")

driver = GraphDatabase.driver(URI, auth=AUTH)

def setup_collab_graph(session):
    session.run("MATCH (n) DETACH DELETE n")
    session.run("""
    CREATE (a:Employee {name: "Arjun Sharma", dept: "Engineering"})
    CREATE (p:Employee {name: "Priya Nair", dept: "Engineering"})
    CREATE (r:Employee {name: "Ravi Kumar", dept: "Logistics"})
    CREATE (m:Employee {name: "Meena Das", dept: "Support"})
    CREATE (k:Employee {name: "Kiran Rao", dept: "Engineering"})
    CREATE (s:Employee {name: "Suresh Pillai", dept: "Logistics"})
    CREATE (a)-[:COLLABORATED_ON {projects: 5}]->(p)
    CREATE (a)-[:COLLABORATED_ON {projects: 3}]->(k)
    CREATE (p)-[:COLLABORATED_ON {projects: 4}]->(k)
    CREATE (p)-[:COLLABORATED_ON {projects: 2}]->(r)
    CREATE (r)-[:COLLABORATED_ON {projects: 1}]->(s)
    """)

def get_collaboration_hubs(session):
    result = session.run("""
    MATCH (emp:Employee)
    RETURN emp.name AS name, emp.dept AS dept,
           size((emp)-[:COLLABORATED_ON]-()) AS connections
    ORDER BY connections DESC
    """)
    return [record.data() for record in result]

def get_isolated_employees(session):
    result = session.run("""
    MATCH (emp:Employee)
    WHERE size((emp)-[:COLLABORATED_ON]-()) <= 1
    RETURN emp.name AS name, emp.dept AS dept
    """)
    return [record.data() for record in result]

def get_cross_dept_bridges(session):
    result = session.run("""
    MATCH (a:Employee)-[:COLLABORATED_ON]-(bridge:Employee)-[:COLLABORATED_ON]-(b:Employee)
    WHERE a.dept <> b.dept AND a <> b
    RETURN DISTINCT bridge.name AS bridge, bridge.dept AS dept,
           collect(DISTINCT a.dept + " <-> " + b.dept) AS connects
    """)
    return [record.data() for record in result]

with driver.session() as session:
    setup_collab_graph(session)

print("Collaboration hubs (by connection count):")
    for r in get_collaboration_hubs(session):
        name = r.get("name", "")
        dept = r.get("dept", "")
        conn = r.get("connections", 0)
        print("  " + name + " (" + dept + "): " + str(conn) + " connections")

print()
    print("Isolated employees (attrition risk):")
    for r in get_isolated_employees(session):
        print("  " + r.get("name", "") + " (" + r.get("dept", "") + ")")

print()
    print("Cross-department bridges:")
    for r in get_cross_dept_bridges(session):
        bridge = r.get("bridge", "")
        connects = r.get("connects", [])
        print("  " + bridge + " bridges: " + "; ".join(connects))

driver.close()

It gives the following output,

Collaboration hubs (by connection count):
  Arjun Sharma (Engineering): 4 connections
  Priya Nair (Engineering): 4 connections
  Kiran Rao (Engineering): 4 connections
  Ravi Kumar (Logistics): 4 connections
  Suresh Pillai (Logistics): 2 connections
  Meena Das (Support): 0 connections

Isolated employees (attrition risk):
  Meena Das (Support)
  Suresh Pillai (Logistics)

Cross-department bridges:
  Priya Nair bridges: Engineering <-> Logistics

For large organizations, use the Neo4j Graph Data Science library: CALL gds.degree.stream('empGraph') returns degree centrality for all nodes in one pass, and gds.betweenness.stream identifies structural bridges automatically. Schedule these analytics as a weekly batch job and write results back to Employee nodes as centrality properties for fast dashboard queries. For ShopMax India, flag employees with zero collaboration connections and high tenure as churn risk - proactively assign them to cross-team projects to increase engagement before they resign.

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