Experience the new ArangoDB Graph Analytics Engine (GAE)
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to discover the power and flexibility of the ArangoDB Data Science Suite.
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on LLMs + Knowledge Graphs.
Advanced Algorithms
Leverage a suite of built-in graph algorithms for advanced analytics. From shortest path to community detection to weakly connected components, ArangoDB provides tools for comprehensive analysis. These algorithms enable the examination of various graph properties and behaviors, providing deep insights into the structure and function of your data.
Enhance Feature Importance with PageRank
PageRank identifies influential nodes in a graph, which is critical for feature selection in the GraphML node classification process. By ranking nodes based on their connectivity, it provides a key feature that highlights node significance, improving the accuracy of predictive models.
Inform Node Groupings with Community Detection
Community detection algorithms uncover clusters of related nodes, which can be used as input features in the GraphML node classification process. Understanding these groupings helps in capturing the underlying structure of the graph, aiding in more precise node classification predictions.
Feature Enrichment with Node Centrality
Node centrality measures, such as betweenness and closeness centrality, offer valuable insights into the roles of nodes within the network. These metrics serve as essential features in the GraphML predictive process, enhancing the model's ability to classify nodes accurately by understanding their relative importance and position in the graph.
Schedule a Demo Today
to discover the power and flexibility of the ArangoDB Data Science Suite.
Read The
Whitepaper
on LLMs + Knowledge Graphs.