90461 [Converted]-1

Tackle Problems that Cannot Be Solved with Traditional Machine Learning

A graph represents relationships between entities such as people, devices, locations, etc. Graph AI applies neural/convolutional network techniques on graphs to provide insights when the relationships between entities is as important as the entities’ attributes themselves.

 

What is Graph AI

 
Graph AI is the science of using Machine Learning on graphs to focus on the relationships between variables
to achieve deeper insights. By using specific algorithms like clustering, partitioning, PageRank and shortest path, some problems
become easier to solve. These include problems where centrality, connectivity, and path analysis play a key role in the analysis.
 
 
 

What Graph AI Can Do for You

interface-symbol-of-three-circles-with-dots-inside-connected-by-two-lines

Interpret many-to-many relationships in unstructured, fluctuating, variable datasets to enhance the accuracy of predictions

interface-symbol-of-three-circles-with-dots-inside-connected-by-two-lines

Enhance classification, pattern detection, and prediction accuracy when combined with AI-ML Graph Neural or Convolutional Networks (GNN/GCN)

interface-symbol-of-three-circles-with-dots-inside-connected-by-two-lines

Support visualizations to express large and complex interconnected data and help you discover relations between entities

How Lynx Analytics Uses Graph AI

Fraud mob

route mob

Network mo

stock mob

ecom mob

 

We are pioneers in the Graph AI space, and we can help you learn how to use Graph AI analytics to achieve your goals. We offer consulting services to deliver tailored data science solutions and help you acquire stronger analytical capabilities.

 

Talk to a Graph Specialist