How does vector search compare to graph-based search?

Vector search and graph-based search are distinct approaches for retrieving data, each suited to different types of problems. Vector search relies on representing data as numerical vectors in high-dimensional space, where similarity is measured using distance metrics like cosine similarity or Euclidean distance. This method is ideal for unstructured data (e.g., text, images) where semantic or contextual similarity matters. For example, finding images visually similar to a query or recommending products based on user preferences. Graph-based search, on the other hand, organizes data as nodes (entities) and edges (relationships), enabling traversal of connections between entities. It excels in scenarios where relationships or paths matter, such as social network friend recommendations or fraud detection by analyzing transaction networks.

Performance and use cases differ significantly. Vector search scales efficiently for large datasets using approximate nearest neighbor (ANN) algorithms like HNSW or IVF, which trade some accuracy for speed. Tools like FAISS or Elasticsearch’s k-NN plugin optimize vector operations, making them suitable for real-time applications like search engines. Graph-based search prioritizes querying complex relationships, often using algorithms like Dijkstra’s shortest path or PageRank. For instance, detecting money laundering might involve tracing multi-hop connections in a financial graph. However, graph traversal can become computationally expensive for large or densely connected datasets, requiring indexing optimizations (e.g., adjacency lists) or graph-specific databases like Neo4j.

The choice between the two depends on data structure and use case requirements. Vector search works best when similarity or semantic relevance is key, and embeddings can be generated effectively (e.g., using models like BERT for text). Graph-based search is preferable when explicit relationships or network topology drive the problem, such as dependency analysis in codebases or route planning. Hybrid approaches are also possible: a recommendation system might use vector search to find similar items and graph traversal to apply rules (e.g., excluding products from blocked vendors). Developers should consider implementation complexity—vector search requires embedding generation and ANN tuning, while graph-based systems demand careful schema design and query optimization. Both methods have trade-offs, but understanding their strengths ensures the right tool for the task.