Traversal

Graph Databases: When to Use Graphs Graph databases store data as nodes and edges, with properties on both. This structure makes it easy to follow connections from one item to another, even through many steps. They shine when relationships are central to the problem, and when the speed of traversing those connections matters more than raw counts of records. Common use cases include social networks, fraud detection, recommendation systems, knowledge graphs, and supply chains. For example, in a social app you might want to find friends of friends who share a hobby, or uncover clusters of users who influence purchases. In a knowledge graph, you link entities like people, places, and events to answer questions quickly. ...

Graph Databases Modeling Relationships and Networks Graph databases store data as nodes and edges. Unlike traditional tables, relationships are first‑class citizens, so connections can be traced quickly as the graph grows. This makes graph databases well suited to modeling networks, social graphs, and complex systems where how elements relate matters as much as the elements themselves. When you model with a graph, you start by naming the kinds of things you care about and the links that connect them. ...

Graph Databases: Modeling Relationships at Scale Graph databases store information as nodes connected by edges, a structure that focuses on how things relate. They use a property graph model, where nodes and edges carry attributes. This makes a single connection meaningful, not just a link in a table. When data is rich in relationships, a graph often stays fast and readable, even as the dataset grows. Modeling for scale means thinking in terms of entities (nodes) and connections (edges). A social network uses Person nodes linked by FRIENDS_WITH edges; an online store connects Product nodes to Category or Review nodes through labeled edges like BELONGS_TO or RATED. The goal is to capture what matters: who is connected to whom, and how those connections influence choices or outcomes. ...

Graph Databases and Connected Data Graph databases store data as nodes and relationships, with properties on both. This mirrors how we see the world: people linked by friendships, products tied to categories, and events connected to places. With this structure, traversing paths of multiple steps becomes natural, not a heavy join in a distant table. They shine when queries focus on connections. You can ask for patterns, short paths, or communities, such as who is connected to a partner company through colleagues, or which customers form a dense network around a product. In fraud detection or recommendations, the value of knowing “who knows whom” or “which item is linked to similar buyers” is clear. ...

Graph Databases and Connected Data Graph databases store data as nodes and the relationships between them. This structure makes it easy to represent connected data in a natural way. Instead of writing many joins, you describe how things are linked and then ask for patterns, paths, or neighborhoods. Nodes can represent people, places, or things; edges show how they relate. Each node and edge can carry properties, such as a person’s name or a friendship since date. This flexibility helps teams model evolving ideas without heavy schema changes. ...