| AllPairsShortestPath (ML Engine) |
Computes the shortest distances between all combinations of the specified source and target vertices. |
| ApproximateCloseness (ML Engine) |
Uses approximate algorithm to find closeness centrality for unweighted graphs with up to 2 billion vertices. |
| Betweenness (ML Engine) |
Determines betweenness for every vertex in a graph. Betweenness is a type of centrality (relative importance) measurement. |
| Closeness (ML Engine) |
Computes closeness and k-degree scores for each specified source vertex in a graph. |
| EigenvectorCentrality (ML Engine) |
Calculates the centrality (relative importance) of each node in a graph. |
| GTree (ML Engine) |
Follows all paths in a graph, starting from a given set of root vertices, and calculates specified aggregate functions along those paths. |
| LocalClusteringCoefficient (ML Engine) |
Analyzes the structure of a network. |
| LoopyBeliefPropagation (ML Engine) |
Calculates the marginal distribution for each unobserved node, conditional on any observed nodes. |
| Modularity (ML Engine) |
Discovers communities (clusters) in input graphs without advance information about the clusters. Detects communities by discovering the strength of relationships among data points. |
| NTree (ML Engine) |
Builds and traverses tree structures on all worker nodes in a graph. |
| PageRank (ML Engine) |
Computes PageRank values for a directed graph. |
| PSALSA (ML Engine) |
Evaluates the similarity of nodes in a bipartite graph according to their proximity. Typically used for recommendation. |
| RandomWalkSample (ML Engine) |
Outputs a sample graph that represents the input graph (which is typically extremely large). |