Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Knowledge graphs and ontologies form the backbone of the Semantic Web by enabling the structured representation and interconnection of data across diverse domains. These frameworks allow for the ...

As the use of graph databases has grown in recent years, ever more applications of this technology involve storing, searching, and reasoning about events. In fact, many companies use this technology ...

Polyglot persistence is becoming the norm in big data. Gone are the days when relational databases were the one store to rule them all; now the notion of using stores with data models that best align ...

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

In the age when data is everything to a business, managers and analysts alike are looking to emerging forms of databases to paint a clear picture of how data is delivering to their businesses. The ...

Let G be a directed graph such that every edge e of G is associated with a positive integer, called the index of e. Then G is called a network graph if, at every vertex v of G, the sum of the indices ...

Carpathian Journal of Mathematics, Vol. 39, No. 1 (2023), pp. 213-230 (18 pages) The normalized distance Laplacian matrix of a connected graph G, denoted by D𝓛(G), is defined by D𝓛(G) = ...