Graph technology is allowing pharma to model data in a way that offers invaluable insights for marketing, R&D and compliance teams alike. Google, Facebook and LinkedIn are among those utilising ...

A line graph is used to spot trends in data over time. In order to produce a line graph, data is required. The data often comes in the form of a table. A student recorded how much time they spent ...

Given that many AI systems are general-purpose, it prompts to consider how to tap into data's full potential for domain-specific applications. BigGraph AI tackles this challenge head-on by employing a ...

CEO and co-founder Emil Eifrem said that Neo4j, which was founded back in 2007, has hit its growth stride in recent years given the rising popularity of graph-based analysis.

Also bringing graphs to the modern data management stack are graph specialists such as Cambridge Semantics, Franz, Neo4j, TigerGraph and others. Multimodel databases have come to support a Swiss ...

Probabilistic graphs and uncertain data analysis represent a rapidly evolving research domain that seeks to reconcile the inherent imprecision of real-world data with robust computational models ...

The field of network visualization and graph analysis has emerged as a crucial interdisciplinary area, merging advanced computational algorithms with sophisticated visual representation techniques ...

Graph neural networks (GNNs) are powerful artificial intelligence (AI) models designed for analyzing complex, unstructured graph data. In such data, entities are represented as nodes and ...

Methods for descriptive network analysis have reached statistical maturity and general acceptance across the social sciences in recent years. However, methods for statistical inference with network ...