What is a Network Graph?

Why can't a table show what a network graph shows?

Relational data is structurally invisible in a table. In relational systems, relationships between entities are distributed across multiple tables and reconstructed at query time through joins. That makes multi-hop analysis complex and harder to scale. A row describes an entity; a table describes a collection of entities. Neither format makes it easy to follow a chain of relationships across multiple hops, identify clusters of connected actors, or spot which single node is holding a network together.

A fraud investigator reviewing hundreds of thousands of transactions in a spreadsheet can flag anomalies in individual rows, but cannot easily see that five apparently unrelated accounts all share the same device ID. The same limitation applies wherever entities interact; criminal networks,fraud or money laundering schemes, intelligence networks, cybersecurity threat maps. In each case, the connection is the insight, and connections do not fit neatly into rows.

Key facts

• A network graph has two required components: a set of nodes representing entities, and a set of edges representing the relationships between them.
• Edges can be directed (A points to B, which is different from B pointing to A) or undirected (the relationship has no inherent direction). The choice depends on what the data represents: a financial transaction has direction; a shared address does not.
• Nodes and edges can each carry additional properties: a node might store a person's name and date of birth; an edge might store the date and amount of a transaction. These properties are what make a network graph analytically useful rather than just visually interesting.
• According to a 2021 paper by Google Research published in Distill, graphs are a natural and general data structure for representing any set of objects and the relationships between them: a description that covers social networks, molecules, knowledge bases, and fraud networks alike.

The following table summarises how network graphs relate to graph databases and knowledge graphs:

How does a network graph work?

Building a network graph starts with two lists. The node list defines every entity, each row is one node with an identifier and any attached properties. The edge list defines every relationship: each row names a source node, a target node, and any properties of the connection.

Think of a detective's evidence board: photographs of individuals pinned to a corkboard (nodes), with string connecting the ones who are known to be linked (edges). The board doesn't tell you everything about each person, it tells you the structure of who is connected to whom. A network graph is the computational equivalent, except it can handle millions of nodes and edges and run algorithms across them in seconds rather than days.

Once the data is structured as nodes and edges, a layout algorithm positions the nodes in two-dimensional space. The most common approach is a force-directed layout, which places densely connected clusters close together and pushes isolated nodes to the periphery, making structural patterns visible without manual arrangement.

The analytical value of a network graph comes from what the layout reveals and what algorithms can compute on top of it:

• Centrality measures identify which nodes are most important, by number of connections (degree centrality), by how often they appear on shortest paths between other nodes (betweenness centrality), or by the importance of their neighbours (eigenvector centrality).
• Community detection algorithms identify clusters of nodes that are more densely connected to each other than to the rest of the graph, revealing groups, rings, or factions within a larger network.
• Path analysis finds the shortest route between two nodes, useful in fraud investigations for establishing indirect connections between entities that appear unrelated.

Without a network graph, answering these questions requires running database queries whose complexity grows exponentially with each additional hop; a network where each node has ten connections reaches 10 nodes at one hop, 100 at two hops, and 1,000 at three. With a graph, the structure itself makes the answer visible, and purpose-built graph architectures can traverse large networks in near-real time rather than hours.

* Performance varies by architecture. Requires purpose-built graph implementation.

What happens to a network graph at enterprise scale?

Network graphs work well when the number of nodes and edges is small enough to display and navigate. As datasets grow into the hundreds of thousands or millions of records, three specific problems emerge.

The first is the hairball problem. When a graph contains too many densely connected nodes, force-directed layouts collapse into an unreadable mass of overlapping lines. The structure is present in the data but invisible in the output. In a fraud investigation, this means the analyst cannot distinguish the key nodes from the noise. Practitioners use the term "hairball" specifically for this failure mode.

The second is entity resolution. In any real-world dataset assembled from multiple source systems, the same real-world entity often appears under different identifiers ("John Smith" in one system, "J. Smith" in another) linked to different account numbers. Before a network graph can be trusted analytically, duplicate nodes must be identified and resolved into a single trusted record.

The third is query performance. In traditional database systems and naïvely implemented graph queries, each additional hop multiplies the search space, causing query times to degrade rapidly at depth. Producing an accurate picture of a large, complex network in near-real time requires an architecture that operates across sets of records simultaneously, storing relationships as explicit, pre-computed structures at ingest time, so that traversal becomes a retrieval operation rather than a computation at query time.

Network graphs add value specifically when the relationship between entities is itself the object of analysis, not when the question is about the attributes of individual records.

Learn more

• Link Analysis Software — how DataWalk's link charts and graph algorithms work in investigative practice.
• Knowledge Graph Software — how DataWalk's ontology-first architecture organises data around real-world entities.
• How DataWalk Compares to Graph Databases, Knowledge Graphs, and Link Analysis Tools — a direct comparison of approaches and what each is suited for.
• Graph Analytics: The New Game-Changer For Anti-Fraud — how graph technology exposes organised fraud rings that rules-based systems miss.
• Graph Embeddings: A Breakthrough For Detecting High-Risk Accounts & Transactions — how network structure is translated into machine learning models for risk scoring.