Graph structures data as nodes - individual entities - and edges, which represent the relationships between the nodes. Because relationships take center stage in a graph data model, network visualization and graph analytics are particularly well suited to understanding and analyzing software dependencies.
In the context of software dependency analysis, nodes represent individual software components or modules, and edges represent how those components relate to each other. Graph visualization gives you a full view over all relationships - both direct and indirect - so you can see each dependency that may cause conflicts.
Graph visualization presents several advantages for developers and IT professionals to manage their software dependencies. A graph doesn’t have a strict schema, giving you flexibility and making it easy to integrate new data. That flexibility also helps break down data silos. Graph visualization tools are also highly scalable, meaning they can be used on large datasets with hundreds or even thousands of elements without any noticeable performance degradation. You can map even the most vast and complex IT and software ecosystems using graph visualization.
Let’s take a look at how this applies in the case of performing impact analysis.