Graph Search vs Vector Search: Which Retrieval Wins?

Vector search answers a question by turning it into an embedding and pulling back the top-k text chunks whose vectors land nearest to it. Graph search answers by starting at named entities and walking the edges between them. The choice between them — graph search vs vector search — is usually framed as a turf war, but the useful way to see it is that they make different mistakes on different question shapes. Top-k similarity is superb at fuzzy recall and hopeless at connection: it will happily hand you chunks that mention A and chunks that mention B while never showing you the path from A to B. Graph traversal reconstructs that path — and struggles where the answer is fuzzy rather than structural.

This is the Search-pillar spoke of our knowledge-graph hub. The conceptual foundation is the cornerstone; the vector-side mirror is Postgres as a Vector Database. Here we’re strictly about the retrieval mechanics — how each one actually finds context, and how to combine them.

Graph search vs vector search — a fuzzy similarity halo of nearby chunks versus a traced multi-hop path through a knowledge graph

🔀 Two Ways to Retrieve

Vector search embeds your query and returns the most similar chunks. It’s the right tool when the relevant text is semantically close to the question — “find me anything about rate-limiting weirdness” surfaces the right passages even without shared keywords. Similarity is its whole model.

Graph search starts at entities and traverses typed relationships: neighborhood expansion (what’s directly connected), multi-hop traversal (what’s connected to what’s connected), shortest-path (how two things relate), and community/subgraph selection (a coherent cluster). The answer is a route, not a ranked pile of text.

The classic illustration is a legal-research question: “how does case X relate to case Y through precedent Z?” Vector search returns the chunk about X, the chunk about Y, and maybe the one about Z — three similar-looking passages with no thread between them. Similarity isn’t connection. Graph traversal walks X → cites → Z → cited_by → Y and hands you the actual relationship. If your question is about how things connect, similarity structurally cannot answer it.

🎯 Match the Retrieval to the Question Shape

The decision isn’t “which is better” — it’s “what shape is the question.”

Vector-native questions (use similarity):

Point lookups and fuzzy recall — “what’s the penalty for breach?”, “find code that parses dates.”
Open-ended semantic search over unstructured text.

Graph-native questions (use traversal):

Relationship — “how is A connected to B?”, “who collaborated with Smith?”
Multi-hop — where the evidence sits two or three hops from any single relevant chunk.
Aggregation / whole-corpus — “what are the main themes across all these documents?” (no single chunk contains a corpus-wide theme).
Blast-radius / structural — “what breaks if I change this?”

Pick the primitive that matches the question, not the infrastructure you happen to have. The tell: if you can’t point to the relationship your question depends on, you probably want similarity; if the answer is a chain or an aggregation, you want the graph.

Matching question shape to retrieval: point and fuzzy questions use vector search, relationship and multi-hop questions use graph traversal

🌐 Local vs Global: The Mental Model to Steal

Microsoft’s GraphRAG ships four query modes, and they’re the clearest public reference for how these strategies differ — worth understanding even if you never use GraphRAG itself:

Local search — start at the entities the query names, then fan out through their graph neighborhood (related entities, relationships, claims) plus the raw chunks those entities came from. Best for “tell me about entity X.”
Global search — a map-reduce over the community summaries the graph was clustered into: answer the question against many community reports, then reduce to one answer. This is what lets a graph answer “what are the main themes across the whole dataset?” — a question class top-k vector search structurally cannot serve, because no single chunk is the answer.
DRIFT search — a global primer (broad community context) that then spawns local follow-ups and drills down. It bridges broad and specific in one query.
Basic search — plain top-k vector RAG, shipped as a baseline inside the same engine.

That last point is the tell for this whole debate: GraphRAG literally includes vector search as a mode. The useful abstraction to carry away is local (entity fan-out) vs global (whole-corpus synthesis) — vector search can only ever do a fuzzy version of local, because it has no representation of the dataset as a whole. (A caveat on the LazyGraphRAG numbers you’ll see quoted — ~0.1% indexing cost, hundreds-of-times cheaper queries — those are Microsoft’s own reported figures; treat them as directional.)

🧩 The Winning Pattern: Entry-Point Recall, Then Structural Expansion

Here’s the retrieval recipe that keeps winning in production, and it is emphatically hybrid: don’t traverse from scratch, because you rarely know the right starting node. Instead:

Find the doorway with recall. Use vector and full-text search to identify good entry-point entities — the candidate nodes most relevant to the query.
Walk the rooms with the graph. From those entry points, traverse the neighborhood, paths, and communities to gather connected context the similarity search would never have reached.

Neo4j’s 2026 production pattern is exactly this: run lexical, vector, and structural searches in parallel, then combine them. And a crucial detail — fuse ranks, not raw scores. A full-text relevance score and a cosine similarity live on different scales and can’t be added directly; the pragmatic default is Weighted Reciprocal Rank Fusion, then rerank the fused set before you spend tokens on traversal.

There’s a bonus use of the graph most people miss: structure as a verification layer. Retrieve candidates by similarity, then confirm an answer only if a real edge or path actually connects the claimed entities. The graph becomes a structural filter against plausible-but-unconnected hallucinations — the retrieval-side complement to the provenance the graph gives you.

The hybrid retrieval recipe: vector and full-text find entry-point entities, then graph traversal expands the connected context

🔧 How Agents Actually Query Graphs Now

The thing that made graph retrieval practical for agents in 2026 is that the agent no longer has to know graph query languages. The dominant surface is Text2Cypher over MCP: a server (like mcp-neo4j-cypher) exposes the graph schema and executes generated Cypher, so the agent calls a tool and gets structured results — or calls typed retrievers (vector, hybrid, VectorCypher) wrapped as tools. Graphify’s server exposes the same idea with query_graph, get_neighbors, and shortest_path. Decoupling the query tool from the agent is what makes graph search composable: the agent picks the retrieval tool that fits the question, and you never hand-write traversal into a prompt.

⚠️ The Catch: Deeper Isn’t Automatically Better

Graph traversal is not free power, and two failure modes matter:

The frontier explodes. Naive k-hop expansion around densely connected “hub” entities pulls in a flood of loosely-related nodes and injects noise. Guardrails: cap the hops, weight edges by query relevance (spreading activation), scope traversal to a community, and truncate after reranking rather than dumping every reachable node into context.
Lost-in-retrieval. If a sub-question is missing its topic entity — the graph never extracted the thing you’re asking about — the hop chain derails. And traversal quality is gated by extraction quality: a relevant entity that was never put in the graph is simply unreachable.

And the honest bottom line, consistent with the rest of this hub: graph search is not universally better. It loses to vector search on fuzzy semantic recall over unstructured text, it costs an expensive upfront extraction pass to build, and it needs ongoing maintenance. One cited order-of-magnitude estimate put indexing a 500-document corpus at ~$5 for vectors vs ~$150 for a graph — directional, not a law, but the shape is real. The answer is almost never graph instead of vector; it’s the two together, each doing what it’s good at.

❓ Frequently Asked Questions

What’s the actual difference between graph search and vector search? Vector search retrieves the most similar text chunks to your query. Graph search starts at entities and traverses relationships — neighborhood, multi-hop, shortest-path. Similarity finds what looks alike; traversal finds what’s connected and how.

When should I use graph search instead of vector search? When the question is about relationships, multi-hop chains, whole-corpus aggregation, or structural blast-radius. For point lookups and fuzzy semantic recall, vector search is simpler and cheaper. Most real systems use both.

What is “local vs global” retrieval? Local retrieval starts at named entities and expands their neighborhood (“tell me about X”). Global retrieval map-reduces over community summaries to answer whole-corpus questions (“what are the themes across everything?”). Vector search can only approximate local — it has no model of the dataset as a whole.

How do I combine graph and vector search? The dominant pattern is entry-point recall then structural expansion: use vector + full-text to find candidate entities, then traverse the graph from them. Fuse the different result lists with weighted Reciprocal Rank Fusion (you can’t add cosine and BM25 scores directly), then rerank.

Do I have to write Cypher to use graph search in an agent? No. The 2026 pattern is Text2Cypher over MCP — the agent calls a tool that exposes the schema and runs generated queries, or calls typed retrievers as tools. The query language is decoupled from the agent.

📚 Further Reading

GraphRAG vs Vector RAG: Knowledge Graphs for AI, Explained — the conceptual cornerstone behind the retrieval mechanics here.
GraphRAG vs Vector RAG: What the Benchmarks Actually Say — whether the graph retrieval described here is worth its cost, by task type.
Postgres as a Vector Database: What the Benchmarks Say — the vector-side mirror, including hybrid keyword+vector search with Reciprocal Rank Fusion.
What Is an Embedding? — the foundation for the “vector” half of this comparison.

📚 Recommended Resources

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