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 ...

Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is characterized by a graph, where nodes represent neural elements such as neurons and brain ...

Paramjit S. Gill, Tim B. Swartz, Bayesian Analysis of Directed Graphs Data with Applications to Social Networks, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 53, No. 2 ...

Graph-based data models have become central to modern machine learning and artificial intelligence applications, and are now widely used by data analysts in applications as diverse as marketing to ...

Why is data visualization so important in statistics, anyway? Graphs and other kinds of visualizations might seem superfluous, if you’re using statistical analysis to look for patterns in a data ...

The four pillars of graph adoption This confluence of graph analytics, graph databases, graph data science, machine learning, and knowledge graphs is what makes graph a foundational technology.

Graphs are essential because it is impossible to navigate through the ocean of unlike data available for modeling and analysis without some tools to illuminate the process.

Siren promises the benefits of knowledge graphs, without the hassle of reinventing your existing data management and infrastructure ...

The global graph analytics market in 2022 was valued at US$1.14 billion. The market value is anticipated to grow to US$6.90 billion by 2028. The market value is expected to grow at a CAGR of 34.80 ...