Staying Ahead: Why Forward-Thinking Companies Are Embracing GNNs and LLMs

In today's data-driven world, staying ahead means leveraging cutting-edge technologies. Two exciting innovations - Graph Neural Networks (GNNs) and Graph Retrieval-Augmented Generation (Graph-RAG) - are poised to transform how we handle complex relational data in Human Capital Management (HCM) and payroll systems.

What are GNNs and Graph-RAG?

Graph Neural Networks (GNNs) are advanced machine learning models designed to work with graph-structured data. They excel at learning patterns and relationships within interconnected data points. Graph Retrieval-Augmented Generation (Graph-RAG) combines graph databases with natural language processing to enable sophisticated querying and information retrieval from graph-structured data.

Why Human Capital Management (HCM) and Payroll Companies Should Care

1. Complex Relationship Modeling: HCM data is inherently relational (employees, departments, roles, etc.). GNNs can automatically learn and leverage these relationships.
2. Predictive Analytics: GNNs can predict trends like employee churn, performance, or career progression based on historical data patterns.
3. Efficient Information Retrieval: Graph-RAG allows for natural language queries on complex HR data structures, making information access more intuitive.
4. Personalized Insights: By understanding the interconnections in your data, these technologies can provide tailored recommendations for employee development, team composition, or compensation strategies.

GNN vs Graph-RAG: When to Use Which?

Use Case	                                     GNN	Graph-RAG
Employee churn prediction	?	
Team performance analysis	?	
Career path recommendation	?	?
Compensation analysis	?	?
Natural language HR queries		?
Real-time org chart updates		?
Multi-factor policy compliance		?
Node classification	                  ?	
Link prediction	                  ?	
Graph classification	                  ?	
Anomaly detection in networks	?	
Molecular property prediction	?	
Social network analysis	?	
Traffic prediction	                  ?	
Recommendation systems	?	?
Knowledge graph completion	?	?
Question answering on structured data.     ?
Complex relationship queries		?
Real-time information retrieval		?
Natural language generation with graph context?
Dynamic graph updates and queries	?
Explainable AI in graph-based systems	?
Multi-hop reasoning on knowledge graphs ?
Graph-based chatbots		?
Fraud detection in financial networks?	?
Drug discovery	                 ?	?
Personalized content generation	?        

Benefits for HCM and Payroll Companies

1. Enhanced Decision Making: Leverage complex data relationships for more informed HR strategies.
2. Improved Efficiency: Automate pattern recognition and data retrieval tasks.
3. Personalized HR: Deliver tailored insights and recommendations at scale.
4. Predictive Capabilities: Anticipate workforce trends and challenges proactively.
5. Natural Language Interfaces: Make HR data more accessible to non-technical users.

By exploring and implementing GNNs and Graph-RAG, HCM and payroll companies can unlock new levels of insight and efficiency in their operations. These technologies offer the potential to transform raw data into strategic assets, driving better decision-making and more personalized HR management.

I encourage you to start exploring these technologies today. The future of HCM is graph-based, and the time to prepare is now!

Sample Code

If you are interested how to get started with GNN, the code below uses a combination of creating, Graph, train a GNN model and use of GPT-4 for NLP.

import torch
import torch.nn.functional as F
import torch.optim as optim
from torch_geometric.nn import GCNConv
from torch_geometric.data import Data
import networkx as nx
import numpy as np
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics.pairwise import cosine_similarity
import openai

# Step 1: Create a sample payroll graph
def create_payroll_graph():
    G = nx.Graph()
    employees = ["Alice", "Bob", "Charlie", "David", "Eve"]
    departments = ["HR", "IT", "Finance", "Marketing"]
    positions = ["Manager", "Developer", "Analyst", "Coordinator"]
    salary_ranges = ["40k-60k", "60k-80k", "80k-100k", "100k+"]
    
    for emp in employees:
        G.add_node(emp, type="employee")
    for dept in departments:
        G.add_node(dept, type="department")
    for pos in positions:
        G.add_node(pos, type="position")
    for sal in salary_ranges:
        G.add_node(sal, type="salary_range")
    
    # Add edges (simplified for demonstration)
    G.add_edge("Alice", "HR")
    G.add_edge("Alice", "Manager")
    G.add_edge("Alice", "80k-100k")
    G.add_edge("Eve", "IT")
    G.add_edge("Bob", "IT")
    G.add_edge("Bob", "Developer")
    G.add_edge("Bob", "60k-80k")
    G.add_edge("Charlie", "Marketing")
    G.add_edge("David", "Marketing")
    # Add more edges as needed
    
    return G

# Step 2: Prepare graph data for PyTorch Geometric
def prepare_pyg_data(G):
    node_mapping = {node: i for i, node in enumerate(G.nodes())}
    edge_index = torch.tensor([[node_mapping[u], node_mapping[v]] for u, v in G.edges()]).t().contiguous()
    
    x = torch.zeros((len(G.nodes()), 4))  # One-hot encoding for node types
    for i, (node, data) in enumerate(G.nodes(data=True)):
        if data['type'] == 'employee':
            x[i, 0] = 1
        elif data['type'] == 'department':
            x[i, 1] = 1
        elif data['type'] == 'position':
            x[i, 2] = 1
        elif data['type'] == 'salary_range':
            x[i, 3] = 1
    
    data = Data(x=x, edge_index=edge_index)
    data.original_graph = G
    data.node_mapping = node_mapping
    return data

# Step 3: Define GNN model
class SimpleGCN(torch.nn.Module):
    def __init__(self, num_node_features, hidden_channels, num_classes):
        super(SimpleGCN, self).__init__()
        self.conv1 = GCNConv(num_node_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, num_classes)

    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        return x

# Step 4: Training function
def train(model, data, optimizer, criterion):
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index)
    loss = criterion(out, data.y)
    loss.backward()
    optimizer.step()
    return loss.item()

# Step 5: Retrieval function
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
language_model = AutoModel.from_pretrained("bert-base-uncased")

def encode_text(text, tokenizer, language_model):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        outputs = language_model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()

query_embedding = encode_text(query, tokenizer, language_model)
print("Query Embedding:", query_embedding)

def retrieve_relevant_info(query, gnn_model, pyg_data, tokenizer, language_model, top_k=3):
    query_embedding = encode_text(query, tokenizer, language_model)
    
    gnn_model.eval()
    with torch.no_grad():
        node_embeddings = gnn_model(pyg_data.x, pyg_data.edge_index)
    
    node_embeddings = F.softmax(node_embeddings, dim=1)
    projected_embeddings = node_embeddings @ torch.randn(4, 768)
    
    similarities = cosine_similarity([query_embedding], projected_embeddings.detach().numpy())[0]
    top_indices = np.argsort(similarities)[-top_k:][::-1]
    
    relevant_info = []
    for idx in top_indices:
        original_node = list(pyg_data.node_mapping.keys())[list(pyg_data.node_mapping.values()).index(idx)]
        node_attributes = pyg_data.original_graph.nodes[original_node]
        node_type = node_attributes['type']
        
        if node_type == 'department' and original_node.lower() in query.lower():
            employees = [n for n in pyg_data.original_graph.neighbors(original_node) if pyg_data.original_graph.nodes[n]['type'] == 'employee']
            relevant_info.append({
                "department": original_node,
                "employees": employees
            })
    return relevant_info

# Step 6: GPT integration
openai.api_key = 'sk-proj-'

def gpt_response(query, relevant_info):
    formatted_info = "\n".join([f"Department: {info['department']}, Employees: {', '.join(info['employees'])}" for info in relevant_info])
    
    prompt = f"""
    Given the following query and relevant information from a graph database:
    
    Query: "{query}"
    
    Relevant Information:
    {formatted_info}
    
    Based on the relevant information, please provide a concise and accurate answer to the query. If the information doesn't directly answer the query, state that clearly. Here are some examples of questions:
    - Who works in the IT department?
    - List all employees working in the Marketing department.
    - Name the employees in the HR department.
    - Which employees are part of the Finance department?

    Please adapt the format accordingly to provide the best possible answer.
    """

    response = openai.ChatCompletion.create(
        model="gpt-4o",      #"gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant that provides information based on graph database queries. Only use the provided information to answer queries."},
            {"role": "user", "content": prompt}
        ]
    )

    return response.choices[0].message['content']

# Main execution
if __name__ == "__main__":
    # Create and prepare data
    graph = create_payroll_graph()
    pyg_data = prepare_pyg_data(graph)
    pyg_data.y = torch.randint(0, 4, (pyg_data.num_nodes,))  # Random labels for demonstration
    
    # Initialize model
    model = SimpleGCN(num_node_features=4, hidden_channels=16, num_classes=4)
    optimizer = optim.Adam(model.parameters(), lr=0.01)
    criterion = torch.nn.CrossEntropyLoss()
    
    # Training loop
    num_epochs = 500
    for epoch in range(num_epochs):
        loss = train(model, pyg_data, optimizer, criterion)
        if (epoch + 1) % 10 == 0:
            print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss:.4f}')
    
    # Example query
    query = "List all employees working in the IT department"
    relevant_nodes = retrieve_relevant_info(query, model, pyg_data, tokenizer, language_model)
    gpt_answer = gpt_response(query, relevant_nodes)
    
    print(f"Query: {query}")
    print("Relevant nodes:", relevant_nodes)
    print("GPT Answer:", gpt_answer)