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 ...

How do you solve the age-old data integration issue? We addressed this in one of the first articles we wrote for this column back in 2016. It was a time when key terms and trends that dominate today's ...

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 ...

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 ...

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) = ...

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 ...

Expanders graphs are sparse but well-connected. These seemingly contrasting properties have led to many applications in theoretical computer science, from complexity ...

The latest information from the National Intellectual Property Administration shows that Shengdi Xingtou Information Technology Co., Ltd., located in the Lhasa Economic and Technological Development ...

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 ...