Graph analytics is the process of analyzing data sets as graphs (or networks in plain language), where data points are expressed as nodes and the relationships between these points are represented as edges.
By using graphs analytics, we can uncover for example the impact of discounting a product on the sell-through of related products. Retailers can use these insights to optimise commercial decisions about discounting, bundling, cross-selling and more.
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.
Graph analytics is the process of analyzing data sets as graphs (or networks in plain language), where data points are expressed as nodes and the relationships between these points are represented as edges.
By using graphs analytics, we can uncover for example the impact of discounting a product on the sell-through of related products. Retailers can use these insights to optimise commercial decisions about discounting, bundling, cross-selling and more.
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.