Vector Databases
What People Misunderstand About Vector Databases
Vector databases are often misunderstood as standalone solutions for all types of data similarity searches. While they excel at identifying semantic similarity through vector embeddings, they lack contextual awareness of how those data points relate to one another.
In other words, vector databases can tell you what is similar, but not why it is similar or how it is connected. This gap becomes apparent when applications require deeper reasoning or contextual analysis, such as multi-hop queries, fraud detection across multiple entities, or network-based threat analysis.
This is where graph databases come in. While vector databases find similarity, graph databases map relationships. The two technologies are complementary: vectors handle high-dimensional similarity, and graphs handle contextual reasoning and relationship mapping. When combined, in a hybrid model, the result is enriched analytics that go beyond raw similarity to provide deeper, more actionable insights. More on that later.
To ground this further, let’s define what a vector database actually is and how it works.
Definition
A vector database is a specialized type of database designed to handle high-dimensional data representations, commonly referred to as vector embeddings. These embeddings are created by machine learning models that numerically encode the essence or meaning of the data.
Imagine each piece of data, whether it’s a document, image, or user profile, as a point in a multi-dimensional space. The more similar two data points are, the closer they appear in this space. For example, two documents discussing similar topics will have vector embeddings that are close together, even if they don’t share identical words. This enables vector databases to perform highly efficient similarity searches, identifying conceptually alike items even when they are not literal matches.
Vector searches are powerful for identifying semantic similarity, but they stop short of understanding how those points are interconnected. For example, vector search can easily identify similar financial transactions, but it cannot reveal if those transactions are part of a broader fraud ring or linked through shared account ownership. This is where graph traversal becomes essential.
To perform this deeper analysis, vector similarity results are sent to a graph database for relationship mapping. In this model, vector databases handle the what—finding the items that are similar—while graph databases handle the why—mapping out the contextual relationships that reveal deeper insights, and:
- Uncover deeper relationships,
- Perform multi-hop analysis across layers of connected data (when available), and
- Enrich the initial vector similarity with contextual insights.
To fully understand how vector databases function in practice, it helps to explore a few core ideas and mechanisms that drive their performance.
Core Concepts
Vector: a sequence of numbers which can be thought of as the coordinates for a point in space
High-Dimensional Information Space: Traditional databases store information in flat tables—rows and columns; but vector databases represent data as points in a multi-dimensional space. Imagine a three-dimensional map, but with hundreds or even thousands of dimensions. In this space, proximity equals similarity. For example, two customer profiles with similar purchasing behavior will be mapped close to each other in vector space, enabling quick discovery of related patterns.
Vector Embeddings: Numerical representations that serve as digital fingerprints for data objects that capture the meaning of data, whether it’s a document, an image, or a user profile, by analyzing patterns, context, and relationships. Similar objects have embeddings that are near each other in vector space, enabling rapid discovery of related items even if they don’t share exact keywords or features.
Similarity Search: The main purpose of a vector database is to find data points that are closest to a given query in vector space. Unlike traditional search, which relies on exact matches, vector search identifies results that are conceptually or semantically similar. This is particularly useful in recommendation systems, natural language processing, and image recognition, where understanding the meaning or context of data is more valuable than exact keyword matching.
Similarity Indexing Mechanisms: To efficiently search through millions or even billions of high-dimensional vectors, vector databases rely on specialized indexing strategies. Two of the most widely used algorithms are HNSW (Hierarchical Navigable Small World) and FAISS (Facebook AI Similarity Search):
HNSW (Hierarchical Navigable Small World): Builds a multi-layered graph where nodes are connected based on their proximity in vector space. This design drastically reduces comparisons, making it highly efficient for large datasets.
FAISS (Facebook AI Similarity Search): Developed by Facebook AI Research, FAISS optimizes fast similarity search and clustering of dense vectors. It is particularly well-suited for image recognition, recommendation systems, and large-scale semantic search.
These indexing mechanisms are critical for maintaining real-time query speeds in vector databases, ensuring performance remains high as datasets grow.
Graph Embeddings vs. Vector Embeddings
Graph embeddings are specific to graph structures and represent the relational context of nodes, like which nodes are connected and how. In contrast, vector embeddings are derived from semantic features, such as text or image analysis, without inherent relationship mapping.
But understanding vectors in isolation isn’t enough—contextual reasoning requires a different approach. Here’s how vector search compares with graph traversal.
Vector Search vs. Graph Traversal
- Vector Search identifies semantic similarities in data points. It maps them in a high-dimensional space in vector databases, such as FAISS or HNSW (these indexing mechanisms are defined below). These searches are great for quickly finding data points that are conceptually similar but lack the ability to explore contextual or multi-hop relationships.
- Graph Traversal goes beyond similarity to explore how and why those data points are connected. This step is crucial for multi-hop analysis, revealing paths, dependencies, and network patterns that vector search alone cannot detect.
Together, vector search finds what is similar, and graph traversal uncovers why those similarities matter in real-world contexts like fraud detection, recommendation engines, and supply chain optimization.
These differences become clearer when we look more closely at what each type of database is designed to do.
Vector Databases vs. Graph Databases
Vector databases and graph databases are often compared, but they solve different types of problems.
Vector databases are optimized for identifying semantic or contextual similarity between data points. They excel at finding items that share similarities based on their characteristics, even if those similarities are not explicitly stated.
Graph databases focus on understanding how and why those data points are connected. They are specifically designed to model complex, real-world scenarios like social networks, fraud rings, or supply chain dependencies.
The difference may appear subtle, but it’s significant: vector databases excel at discovering what is similar, while graph databases excel at understanding why it is connected. This makes graph databases particularly powerful for applications that demand both similarity and contextual mapping, such as fraud detection, knowledge graphs, and real-time recommendation engines.
Of course, the real power emerges when these technologies are combined. That’s where hybrid search comes in.
Hybrid Search: Graph + Vector
Hybrid Search bridges the gap between what is similar and why it is connected by combining vector embeddings with graph traversal. Vector similarity search is handled externally, typically in a vector database or machine learning model. Once vector-based similarities are retrieved, graph traversal maps relationships, uncovers dependencies, and performs multi-hop analysis, as available.
This process is not limited to just finding similar items. It uncovers how those items are interconnected, such as understanding if similar transactions share common account owners, or if similar suppliers are connected through shared distribution networks. This hybrid approach is valuable for:
- Real-time threat detection – by identifying how threat patterns propagate across networks.
- Fraud analysis – by revealing cross-network laundering and multi-account fraud rings.
- Supply chain optimization – by mapping hidden dependencies to anticipate disruptions.
This hybrid model drives value across industries. Let’s take a look at how it plays out in the real world.
Use Cases
The following use cases illustrate how vector similarity and graph traversal complement each other to achieve deeper insights and more effective analytics:
- Recommendation Systems: Vector databases excel at identifying products, services, or content that are similar based on user behavior or textual content. For example, if a user watches certain types of movies, a vector search can find films with similar themes, even if they have different titles or descriptions. This allows recommendation engines to surface content that matches user preferences even when explicit tags or keywords are missing.
When combined with graph traversal, recommendation engines can uncover deeper contextual links, like shared user groups, mutual connections, or even influence patterns that surface more relevant suggestions.
- Fraud Detection: Vector databases can flag suspicious transactions based on similarity patterns, like unusual account activity or repeated high-value transactions.
When combined with TigerGraph’s graph traversal, this analysis expands to uncover hidden fraud rings, shared account ownership, and cross-network laundering schemes that isolated similarity searches would miss. Its multi-hop analysis traces connections across layers of financial networks, revealing deeper vulnerabilities and coordinated attacks.
- Semantic Search and Document Retrieval: Vector databases excel at searching unstructured data like documents, emails, or web pages. By capturing the semantic meaning rather than just keywords, vector search can surface relevant documents even when the exact terms do not match. For example, a search for “remote work best practices” can retrieve articles about “virtual team management” or “distributed workforce strategies.”
Graph traversal can layer on connections, such as linking documents to authors, organizations, and the relationships between those entities, to provide richer context and discover related content that a simple keyword search would overlook.
- Real-Time Threat Detection: In cybersecurity, identifying threats often involves spotting patterns across dispersed systems and networks. Vector databases can do exactly that—detecting similarities in threat signatures and behavior anomalies, and alerting companies to repeated login attempts or synchronized transactions across regions.
Graph traversal expands this analysis, revealing the threat’s propagation path, potential lateral movement, and network vulnerabilities. It offers a layered view that accelerates incident response and improves threat containment strategies.
- Supply Chain Optimization: Vector search can identify suppliers or logistical nodes with similar performance metrics or risk profiles.
When enhanced with graph traversal, supply chain mapping can expose critical dependencies, bottlenecks, and potential points of failure. For example, suppose two suppliers are identified as having similar shipment delays. Graph traversal can reveal whether they share common distributors or raw material sources, helping organizations proactively manage risk and optimize resource allocation, anticipating the ripple effects of supplier disruptions.
These examples point to a bigger trend: the evolution of vector databases toward hybrid, context-aware solutions.
Vector Databases Evolving with a Hybrid Approach
Vector databases have introduced powerful capabilities for identifying semantic similarity across vast datasets, enabling fast and scalable similarity searches. However, they are primarily focused on recognizing what is similar and do not inherently understand why those similarities exist or how the data points are connected.
This is where graph databases like TigerGraph add value. By mapping the connections between data points, graph traversal complements vector search by revealing the contextual paths and dependencies between similar entities. It’s a richer analysis that extends beyond similarity, uncovering deeper relationships, multi-hop dependencies, and contextual insights that are critical for advanced applications such as fraud detection, supply chain optimization, and real-time threat detection.
Powerful graphs enable organizations to extend vector-based similarity searches into graph-powered contextual analysis. This hybrid approach bridges the gap between similarity and understanding, allowing organizations to not only identify similar data points but also reveal the hidden relationships and dependencies that make those similarities significant. This deeper layer of analysis unlocks opportunities for fraud detection, real-time threat analysis, and supply chain optimization by exposing critical paths, cascading risks, and multi-hop relationships that are invisible to isolated vector searches.
