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 ...

Such studies furnish robust algorithms capable of handling a wide range of graph types, thereby opening new avenues for analysing the interplay between algebraic invariants and graph structure [3].

Technical Terms Graph Algorithms: Methods and procedures for solving problems related to graph structures, including optimisation, connectivity, and flow analysis.

Now imagine that you want to insert a new edge connecting two nodes in a planar graph, say nodes 1 and 6 in the example below. To do so, you’re going to perform a series of flips. From the starting ...

Personalized recommendations can be enhanced with graph algorithms to find customer communities based on common purchase behavior. Based on graph algorithms, you can begin to make a variety of ...

KAIST’s tool – which is named “Trillion-scale Graph Processing Simulation,” or T-GPS – bypasses the storage step. Instead, T-GPS loads the smaller, real graph into its main memory. Then, it runs the ...

In other words, a knowledge graph is a programmatic way to model a knowledge domain with the help of subject-matter experts, data interlinking, and machine learning algorithms.

Graph analytics and knowledge graphs facilitate scientific research for COVID-19 State of the art in analytics and AI can help address some of the most pressing issues in scientific research.

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 ...