Dynamic graph algorithms and data structures represent a vital research frontier in computer science, underpinning applications from network analysis to real-time system monitoring. These methods ...

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

In our use case, we had to build a graph data structure with subjects, relations, and objects. To do this, we used many basic data structures in Go, such as structs, slices, maps, and strings.

Graph analytics such as PageRank can be applied to data stored in any back end. Graph databases are back ends designed to accommodate graph data structures, offering specialized query languages ...

Graphs -- data structures that show the relationship among objects -- are highly versatile. It's easy to imagine a graph depicting a social media network's web of connections.

This graph-shaped amalgamation of data points, relationships, metadata, and meaning is what we call a knowledge graph. Google introduced the term in 2012, and it’s now used far and wide.

This makes data structures one of the most fundamental components of computer science, and this is why they are at the core of all subfields, including data systems (relational, key-value, graph ...

It involves understanding metadata knowledge graphs and how different layers of the modern data stack come together. If one wants to do anything with data, they need a stack of tools to get it done.