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Classes

AllPairsShortestPaths: Finds shortest paths between pairs of multiple sources and sinks.
AllPairsShortestPathsResult: Represents all shortest paths between pairs of source and sink nodes as computed by AllPairsShortestPaths.
BetweennessCentrality: Computes the betweenness centrality for each node and edge of a given graph.
BetweennessCentralityResult: Represents the node and edge centrality values as computed by BetweennessCentrality.
Bfs: Computes the layers of nodes constructed by a directed or undirected breadth-first search.
BfsLayer: Represents a layer of nodes as a result of a breadth-first search.
BfsResult: Represents the results of a breadth-first search as computed by Bfs.
BiconnectedComponent: A single biconnected component, composed of the nodes and edges which belong to this component and its articulation points.
BiconnectedComponentClustering: Partitions the graph into clusters by analyzing its biconnected components.
BiconnectedComponentClusteringResult: Represents clusters in the graph as computed by BiconnectedComponentClustering.
BiconnectedComponents: Determines the biconnected components and articulation points of a given undirected graph.
BiconnectedComponentsResult: Represents the biconnected components of a graph, as computed by BiconnectedComponents.
Bipartition: Calculates a bipartition of the given graph, if one exists.
BipartitionPartition: Represents a partition in a bipartite graph.
BipartitionResult: Represents a bipartition of a graph.
ChainSubstructures: Detects SubstructureItems that represent isolated chains in the specified graph.
ChainSubstructuresResult: Represents the chains found by ChainSubstructures.
Chains: Finds all chains in a graph.
ChainsResult: Represents the chains found in the graph as computed by chains.
CliqueSubstructures: Detects SubstructureItems that represent the (undirected) cliques in the specified graph.
CliqueSubstructuresResult: Represents the cliques found by CliqueSubstructures.
ClosenessCentrality: Computes the closeness centrality for the nodes of a graph.
ClosenessCentralityResult: Represents the node centrality values as computed by ClosenessCentrality.
Cluster: Represents a cluster, that is, a group of nodes that by some measure belong closely together.
ClusteringCoefficient: Calculates the local clustering coefficient for each node and returns the average clustering coefficient.
ClusteringCoefficientResult: Represents the clustering coefficients in the graph as computed by ClusteringCoefficient.
ConnectedComponent: A single connected or strongly connected component, composed of the nodes and inducedEdges which belong to this component.
ConnectedComponents: Determines the connected components of a given graph.
ConnectedComponentsResult: Represents the connected components of a graph.
Cycle: Finds a directed or undirected cycle of edges in a graph.
CycleEdges: Finds all edges that are part of at least one directed or undirected simple cycle in the graph.
CycleEdgesResult: Represents edges that are part of at least one directed or undirected simple cycle as computed by CycleEdges.
CycleResult: Represents a cycle's nodes and edges as found by Cycle.
CycleSubstructures: Detects SubstructureItems that represent the isolated cycles in the specified graph.
CycleSubstructuresResult: Represents the cycles found by CycleSubstructures.
Dart: Represents a dart of a face of a PlanarEmbedding.
DegreeCentrality: Computes the degree centrality for the nodes of a given graph.
DegreeCentralityResult: Represents the node centrality values as computed by DegreeCentrality.
DendrogramNode: Represents a node of the dendrogram which is a binary tree of clusters after HierarchicalClustering.
EdgeBetweennessClustering: Partitions the graph into clusters using edge betweenness centrality, as proposed by Girvan and Newman.
EdgeBetweennessClusteringResult: Represents clusters in the graph as computed by EdgeBetweennessClustering.
EigenvectorCentrality: Computes an eigenvector centrality for each node of a given undirected, unweighted graph.
EigenvectorCentralityResult: Represents the node centrality values as computed by EigenvectorCentrality.
FeedbackEdgeSet: Finds edges of a given graph whose removal or reversal would make the graph acyclic (also called Feedback Arc Set).
FeedbackEdgeSetResult: Represents edges that can be removed or reversed in a graph to make it acyclic as computed by feedbackEdgeSet.
GraphCentrality: Computes the graph centrality for the nodes of a graph.
GraphCentralityResult: Represents the node centrality values as computed by GraphCentrality.
GraphStructureAnalyzer: This class provides methods that check structural properties of a given graph.
HierarchicalClustering: Partitions the graph into clusters using hierarchical clustering.
HierarchicalClusteringDendrogram: Represents the result of a hierarchical clustering algorithm in the form of a binary tree structure.
HierarchicalClusteringResult: Represents clusters in the graph and the dendrogram as computed by HierarchicalClustering.
IndependentSet: Represents a single independent set of nodes in a graph.
IndependentSets: Partitions the set of nodes of the given graph into independent sets.
IndependentSetsResult: Represents a partitioning of a graph into independent sets of nodes.
Intersection: Represents an intersection as calculated by Intersections.
Intersections: Finds all intersections between nodes, edges, and labels.
IntersectionsResult: Holds all intersections as calculated by Intersections.
KCoreComponents: Calculates the k-cores of an undirected input graph.
KCoreComponentsResult: Represents the k-Cores as computed by KCoreComponents.
KMeansClustering: Partitions the graph into clusters using k-means clustering.
KMeansClusteringResult: Represents clusters in the graph as computed by KMeansClustering.
KShortestPaths: Computes the k shortest paths connecting a pair of nodes in a directed graph with non-negative edge costs.
KShortestPathsResult: Represents all shortest paths between pairs of source and sink nodes as computed by KShortestPaths.
LabelPropagationClustering: Detects the communities in the specified input graph by applying a label propagation algorithm.
LabelPropagationClusteringResult: Represents clusters in the graph as computed by LabelPropagationClustering.
LayoutGraphAlgorithms: Provides a collection of algorithms for analyzing and manipulating a LayoutGraph within the context of layout processing.
LayoutGraphIntersection: Describes an intersection between two graph items.
LayoutGraphNodeAggregation: This class realizes an algorithm that aggregates nodes in a LayoutGraph and creates a hierarchical clustering structure subject to user-specified constraints like the type of nodes as well as the preferred minimum and maximum size of a cluster.
LongestPath: Finds the longest directed path in an acyclic graph.
LongestPathResult: Represents the longest directed path in an acyclic graph as computed by LongestPath.
LouvainModularityClustering: Detects the communities in the specified input graph by applying the Louvain modularity method.
LouvainModularityClusteringResult: Represents clusters in the graph as computed by LouvainModularityClustering.
MaximumFlow: Solves a maximum flow problem.
MaximumFlowResult: Represents the maximum flow through edges of a graph as computed by maximumFlow.
MinimumCostFlow: Solves a minimum-cost flow problem.
MinimumCostFlowResult: Represents a minimum-cost flow through a graph as calculated by MinimumCostFlow.
Neighborhood: Finds the direct or indirect neighbors of a given set of nodes.
NeighborhoodResult: The direct or indirect neighbors of a given set of startNodes.
NodeAggregate: Represents an aggregate or cluster found by NodeAggregation.
NodeAggregation: Aggregates the nodes of a given graph and creates a hierarchical clustering structure subject to user-specified constraints.
NodeAggregationInfo: The result of an aggregate run is a hierarchical nested clustering structure.
NodeAggregationResult: The tree of NodeAggregate which is created by NodeAggregation.
PageRank: Computes page rank values for all nodes based on their attached edges.
PageRankResult: Represents the page rank values as computed by PageRank.
Path: Represents a path between two nodes.
Paths: Finds all simple directed or undirected paths between one or more startNodes and endNodes.
PathsResult: Represents all paths between two sets of nodes as computed by paths.
PlanarEmbedding: Represents an embedding of a planar graph.
RankAssignment: Solves the rank assignment problem.
RankAssignmentRank: Represents a group of nodes with the same rank.
RankAssignmentResult: Represents a ranking for nodes in a graph as computed by RankAssignment.
Reachability: Determines the nodes that are reachable from one or more startNodes.
ReachabilityResult: Represents the nodes that are reachable from a given set of other nodes as computed by Reachability.
ResultItemCollection: Represents an ordered or unordered collection of IModelItems that is part of a result of running a graph analysis algorithm.
ResultItemMapping: Represents a mapping from keys to values that is part of a result of running a graph analysis algorithm.
ShortestPath: Finds the shortest path between two nodes (also known as the single-source single-sink shortest path problem).
ShortestPathResult: Represents the shortest path between two nodes in a graph as computed by ShortestPath.
SingleSourceShortestPaths: Finds the shortest path between one node and multiple other nodes in the graph (also known as the single-source shortest path problem).
SingleSourceShortestPathsResult: Represents all shortest paths between a single source node and multiple sink nodes in a graph as computed by SingleSourceShortestPaths.
SpanningTree: Calculates a minimum spanning tree or forest for a graph.
SpanningTreeResult: Represents the edges of a minimum spanning tree as calculated by SpanningTree.
StarSubstructures: Detects SubstructureItems that represent isolated stars in the specified graph.
StarSubstructuresResult: Represents the stars found by StarSubstructures.
StronglyConnectedComponents: Determines the strongly connected components of a given graph.
StronglyConnectedComponentsResult: Represents the strongly connected components of a graph.
Substructure: Represents a substructure of a graph, like a tree, chain, star, clique, or tree.
SubstructureItems: Represents a substructure of a graph, like a tree, chain, star, or clique.
Subtree: Represents a subtree computed by methods getSubtree or getDescendants.
TransitiveClosure: Calculates the transitive closure for a directed acyclic graph.
TransitiveClosureResult: Represents the edges that have to be added to obtain the transitive closure of a graph as computed by TransitiveClosure.
TransitiveEdge: Represents a placeholder for an edge that does not yet exist in a graph.
TransitiveEdges: Calculates the transitive edges that connect the visibleNodes in the specified graph.
TransitiveEdgesResult: Represents the edges that have to be added to obtain the transitive edges of a graph as computed by TransitiveEdges.
TransitiveReduction: Calculates the transitive reduction for a directed acyclic graph.
TransitiveReductionResult: Represents the transitive edges of a graph as computed by TransitiveReduction.
TreeAnalysis: Analyzes a tree graph and calculates important properties of the tree structure.
TreeAnalysisResult: Represents the analysis result computed by TreeAnalysis.
TreeSubstructures: Detects SubstructureItems that represent isolated trees in the specified graph.
TreeSubstructuresResult: Represents the trees found by TreeSubstructures.
WeightCentrality: Computes the weight centrality for the nodes of a graph.
WeightCentralityResult: Represents the node centrality values as computed by WeightCentrality.

Enums

HierarchicalClusteringLinkage: Provides specifiers to define how the distance between clusters is determined.
IntersectionItemTypes: Graph item types for the intersection algorithm findIntersections.
KMeansDistanceMetric: Provides constants for different distance metrics for the kMeansClustering.
NodeAggregationPolicy: Specifies how the nodes are aggregated by the node aggregation algorithm.
NodeTypePolicy: Policies that define how node types are handled by the node aggregation algorithm.
TraversalDirection: Specifies constants for defining the search direction along edges.