The in-memory graph server layer enables you to analyze property graphs using parallel in-memory execution. It provides over 60 analytic functions. Examples of the categories and specific functions include:

• Centrality - Degree Centrality, Eigenvector Centrality, PageRank, Betweenness Centrality, Closedness Centrality
• Component and Community - Strongly Connected Components (Tarjan's and Kosaraju's). Weakly Connected Components
• Twitter's Who-To-Follow, Label Propagation.
• Path Finding - Single source all destination (Bellman-Ford), Dijsktra's shortest path, Hop Distance (Breadth-first search)
• Community Evaluation - Coefficient (Triangle Counting), Conductance, Modularity, Adamic-Adar counter.

See Using the In-Memory Graph Server (PGX) for more information on the in-memory graph server (PGX).