Graph Reasoner Sample Notebooks

Explore sample notebooks demonstrating the use of graph reasoners in various applications.

Betweenness Centrality Discover bridge nodes in a social network using betweenness centrality with RAI. Visualize and query a graph built from your model.

Degree Centrality Find the most connected individuals in your network using degree centrality in RAI. Analyze and visualize social influence at a glance.

Degree Analyze node connectivity in your graph with RAI. Compute degree, in-degree, and out-degree to surface structural insights.

Eigenvector Centrality Go beyond connection counts—use eigenvector centrality in RAI to identify the most influential nodes in your graph network.

Infomap Discover subgroups in your social graph using Infomap in RAI. Visualize how shared traits form distinct network communities.

Jaccard Similarity Find overlap in node relationships with Jaccard similarity in RAI. Highlight pairs with the most in common in your graph.

Label Propagation Find natural clusters in your network with RAI’s label propagation algorithm. Compare results across weighted and unweighted graphs.

Louvain Explore hierarchical clustering in RAI using Louvain. Discover social groups and see how weights refine community detection.

PageRank Apply PageRank in RAI to explore how influence flows through your graph. Identify nodes that gain authority from key connections.

Triangle Community Identify core clusters in your graph with RAI’s triangle detection. Perfect for spotting strongly linked communities.

Weakly Connected Components Detect loosely connected groups with RAI’s weakly connected components. Nodes are grouped if any path connects them, regardless of direction.