Semantic and Similarity-Driven Graph Searches

Sometimes, the user doesn't know what they are looking for. They don't have a name, a date, or an ID. They have a "Vibe."

• "Find me projects that feel similar to the old Valkyrie experiment."

In this lesson, we will look at Similarity-Driven Retrieval. We will learn how to use Node Embeddings to find similar nodes across the graph, how to use the k-Nearest Neighbor (kNN) algorithm inside the database, and how to use these similarity links to move between disparate communities that aren't physically "Connected" by an edge.

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1. The "Virtual Edge": Semantic Similarity

In a standard graph, an edge is an explicit fact: (A)-[:WORKS_AT]->(B). In a Semantic Graph, we can also think of an "Implicit Edge"—a link based on how similar two nodes' descriptions are.

The Strategy:

1. Embed Entire Nodes: Instead of embedding just the text, you embed the Node Properties (e.g., all facts about Project A).
2. Vector Comparison: During retrieval, you find nodes that are semantically close to the user's query vector.
3. Bridge to Topology: Use those nodes as "Seeds" to start a standard graph walk.

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2. Using SimRank and Node2Vec

There are algorithms that calculate similarity based on Graph Structure, not just text.

• Node2Vec: Converts a node's "Position" in the graph into a vector. If two nodes have the same neighbors, their vectors will be similar even if their names are different.
• SimRank: "Two nodes are similar if they are connected to similar nodes."

RAG Case: Useful for finding "Duplicate Entities" or "Analogous Projects" across different departments that use different terminology.

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3. The "Similarity Expansion" Pattern

Traditional expansion: "Find neighbors of A." Semantic expansion: "Find nodes similar to A, AND then find their neighbors."

This retrieval strategy is excellent for Ideation and Discovery. It allows the AI agent to say: "Project Phoenix is what you're looking for. It's not connected to Valkyrie, but it uses the same propulsion logic and facing similar budget risks."

graph LR
    A[Node: Valkyrie] -. Semantic Similarity .-> B[Node: Phoenix]
    B --> C[Fact 1]
    B --> D[Fact 2]
    
    subgraph "Vector Search Bridge"
    A --- B
    end
    
    style A fill:#4285F4,color:#fff
    style B fill:#34A853,color:#fff

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4. Implementation: A Similarity-Seeded Walk in Python

Let's look at the logic flow for a "Vibe-based" graph search.

def semantic_graph_search(user_query, k=3):
    # 1. Get the query vector
    q_vec = get_embedding(user_query)
    
    # 2. Find the 'Semantic Seed' in the Graph DB
    # Using the Vector Index from Module 7
    query = """
    CALL db.index.vector.queryNodes('node_embeddings', $k, $q_vec)
    YIELD node as seed, score
    
    // 3. Perform a 1-hop expansion for context
    MATCH (seed)-[r]-(neighbor)
    RETURN seed.name as concept, 
           type(r) as relationship, 
           neighbor.name as detail
    """
    
    return db.run(query, {"q_vec": q_vec, "k": k})

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5. Summary and Exercises

Semantic search makes your graph Intuitive.

• Node Embeddings translate graph concepts into "Searchable Vectors."
• Structure-based similarity (Node2Vec) finds patterns in connectivity.
• Hybrid Expansion allows discovery across disconnected islands of the graph.
• kNN is the "Entrance Door" for users who don't know the exact names of things.

Exercises

1. Similarity Duel: You are looking for a project about "Mars." Node A is named Mars-Probe. Node B is named Red-Planet-Explorer. Which one will an Exact Match find? Which one will a Semantic Search find?
2. Structural Similarity: If two people have the exact same 10 friends, but they have never met, are they "Similar" in the graph? Why is this useful for a recommendation bot?
3. Visualization: Draw two "Islands" of nodes that have no edges between them. How would you "Jump" from Island 1 to Island 2 using a vector search?

In the next lesson, we will finalize Module 9 with the big picture: Strategy Selection: Which Pattern for Which Question?.