And compute power has increased a trillion-fold, which makes it easier to recreate these algorithms at scale. Knowledge graphs and graph database technology are necessary to better understand data.

I’ve been studying graphs for a while and although it’s always been interesting to hear people talk about graph analytics, it has never been clear to me precisely what is meant. Often when you ...

The graph database was originally designed to store networks — that is, the connections between several elements such as people, places they might visit, or the things they might use.

In algorithms, as in life, negativity can be a drag. Consider the problem of finding the shortest path between two points on a graph — a network of nodes connected by links, or edges. Often, these ...

Katie Roberts, PhD, data science solution architect at Neo4j, joined DBTA's webinar, 'Solving Data Challenges with Knowledge Graphs and Context-Aware Recommendation Systems,' to explore how building ...

From the 18th century to today, a number of graph algorithms have been developed. Path finding, centrality, community detection and similarity are some of the main classes of graph algorithms.