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Graph database

A graph database, also called a graph network or a semantic database, is specifically designed to help the user identify and understand relationships (or links) that exist between different points of data. 

Graph databases are often designed to be visual in nature. When a user runs a query, the output will be in the form of a graph that makes it easy to visualize how the different pieces of data are related to one another. With this in mind, graph databases typically look something like this:

By comparison, a traditional database stores data in tables. These tables are highly effective at storing and retrieving large quantities of information but are not easily used to determine relationships between pieces of data.

Frequently asked questions

Graph databases consist of five key parts:

  • The data: As with all databases, graph databases first and foremost are a store of data. This data can take virtually any form; it simply depends on what kind of data a business collects from its users. Personal data, payment data, business data, and a user’s activity can all be considered. Ideally, a graph database should be able to ingest as much data as possible in order to maximize its chances of catching fraud.

  • Nodes: A node represents an entity or object that is found in the data, such as account, user, location, business, etc. Nodes are often depicted visually as circles. 

  • Labels: A label is simply a means of describing a node. Labels make it possible to group similar nodes together so any relationships between them can be understood. For example, a single graph database may include nodes that represent both people and businesses. Labels would make it possible to differentiate between these two types of nodes.

  • Properties: A property is a piece of data about a specific node. For example, a social network might use properties to describe a user’s name, gender, location, and contact information. 

  • Edges: An edge is a relationship that exists between two or more nodes. These relationships can take many different forms depending on the data stored about each node. An edge is typically depicted as a straight line connecting a node. 

Nodes, labels, properties, and edges make link analysis possible.

Link analysis, which is facilitated by a graph database, can be leveraged for any use case that requires a business to understand how pieces of data are connected to one another. Common use cases include:

  • Fraud detection and analysis

  • Identity and access management

  • Network and database management

  • Data privacy 

  • Compliance and risk management

  • Supply chain management

  • Search engine management

  • Product recommendation systems

Graph databases underpin link analysis, a powerful data science technique that businesses can use to identify fraudulent accounts and activity.

Graph databases organize and label data in a way that makes it easier to understand how objects are related to one another. Link analysis looks at these connections in order to surface suspicious links between accounts. Once suspicious accounts are surfaced and flagged, they can be investigated — and if fraudulent activity is found, the accounts can be removed. 

Link analysis can also be highly effective at uncovering large fraud rings after a single fraudulent account has been identified. Fraudsters often use the same details (physical address, payment details, etc.) to open multiple accounts on a platform, or share and trade details with other fraudsters who then open their own accounts. With this in mind, a graph database can be a powerful tool for fully removing a bad actor from your platform once they have been found.