Network-Analysis

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 for Connected Data In many apps, data comes with many links. Users connect to friends, products relate to categories, and devices talk to services. A graph database stores not only items but also the links between them. This makes it easier to answer questions like who is connected to whom, or which paths lead to a goal. Compared with traditional databases, graph stores focus on relationships. Data is modeled as nodes (entities) and edges (relationships). You can add properties to both nodes and edges to describe details like a person’s age or the strength of a connection. With this setup, traversing a network becomes fast, even when the data grows large. ...