The OPTGRAPH Procedure
- Overview
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Getting Started
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SyntaxFunctional SummaryPROC OPTGRAPH StatementBICONCOMP StatementCENTRALITY StatementCLIQUE StatementCOMMUNITY StatementCONCOMP StatementCORE StatementCYCLE StatementDATA_LINKS_VAR StatementDATA_MATRIX_VAR StatementDATA_NODES_VAR StatementEIGENVECTOR StatementLINEAR_ASSIGNMENT StatementMINCOSTFLOW StatementMINCUT StatementMINSPANTREE StatementPERFORMANCE StatementREACH StatementSHORTPATH StatementSUMMARY StatementTRANSITIVE_CLOSURE StatementTSP Statement
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DetailsGraph Input DataMatrix Input DataData Input OrderParallel ProcessingNumeric LimitationsSize LimitationsCommon Notation and AssumptionsBiconnected Components and Articulation PointsCentralityCliqueCommunityConnected ComponentsCore DecompositionCycleEigenvector ProblemLinear Assignment (Matching)Minimum-Cost Network FlowMinimum CutMinimum Spanning TreeReach (Ego) NetworkShortest PathSummaryTransitive ClosureTraveling Salesman ProblemMacro VariablesODS Table Names
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ExamplesArticulation Points in a Terrorist NetworkInfluence Centrality for Project Groups in a Research DepartmentBetweenness and Closeness Centrality for Computer Network TopologyBetweenness and Closeness Centrality for Project Groups in a Research DepartmentEigenvector Centrality for Word Sense DisambiguationCentrality Metrics for Project Groups in a Research DepartmentCommunity Detection on Zachary’s Karate Club DataRecursive Community Detection on Zachary’s Karate Club DataCycle Detection for Kidney Donor ExchangeLinear Assignment Problem for Minimizing Swim TimesLinear Assignment Problem, Sparse Format versus Dense FormatMinimum Spanning Tree for Computer Network TopologyTransitive Closure for Identification of Circular Dependencies in a Bug Tracking SystemReach Networks for Computation of Market Coverage of a Terrorist NetworkTraveling Salesman Tour through US Capital Cities
- References
Functional Summary
Table 1.2 summarizes the statements and options available with PROC OPTGRAPH.
Table 1.2: Functional Summary
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Description |
Option |
|---|---|
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Input |
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Specifies the link data set |
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Specifies the matrix data set |
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Specifies the node data set |
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Specifies the node subset data set |
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Output |
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Specifies the link output data set |
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Specifies the node output data set |
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Options |
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Specifies the subgraph filter level |
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Specifies the graph direction |
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Specifies the internal graph format |
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Includes self links |
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Specifies the overall log level |
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Specifies whether time units are in CPU time or real time |
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Data Input Statements |
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Specifies the data set variable name for the from nodes |
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Specifies the data set variable name for the link flow lower bounds |
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Specifies the data set variable name for the to nodes |
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Specifies the data set variable name for the link flow upper bounds |
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Specifies the data set variable name for the link weights |
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Specifies the data set variable names for the matrix |
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Specifies the data set variable name for cluster identifiers |
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Specifies the data set variable name for the nodes |
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Specifies the data set variable name for node weights |
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Specifies the data set variable name for auxiliary node weights |
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Algorithm Statements |
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Specifies the log level for biconnected components |
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Calculates authority centrality and specifies the type to process |
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Calculates betweenness centrality and specifies the type to process |
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Specifies whether to normalize the betweenness calculation |
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Decomposes the calculations for centrality by cluster (or subgraph) |
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Calculates closeness centrality and specifies the type to process |
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Specifies the accounting method for no paths in closeness |
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Calculates the node clustering coefficients |
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Calculates degree centrality and specifies the type to process |
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Calculates eigenvector centrality and specifies the type to process |
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Specifies the algorithm to use for eigenvector calculation |
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Specifies the maximum number of iterations for eigenvector calculation |
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Calculates hub centrality and specifies the type to process |
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Calculates influence centrality and specifies the type to process |
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Specifies the iteration log frequency (nodes) |
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Specifies the iteration log frequency (seconds) |
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Specifies the log level for centrality |
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Specifies the subgraph node size to run separately |
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Specifies the data set variable to use for weight2 in centrality |
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Specifies the log level for clique calculations |
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Specifies the maximum number of cliques to return during clique calculations |
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Specifies the maximum amount of time to spend calculating cliques |
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Specifies the output data set for cliques |
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Specifies the community detection algorithm |
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Specifies the percentage of small-weight links to be removed |
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Specifies the log level for community detection |
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Specifies the maximum number of iterations for community detection |
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Specifies the output data set for between-community links |
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Specifies the output data set for community summary table |
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Specifies the output data set for community level summary table |
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Specifies the output data set for community overlap table |
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Specifies the random factor in the parallel label propagation algorithm |
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Specifies the random seed for the parallel label propagation algorithm |
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Applies the recursive option to break large communities |
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Specifies the resolution list for community detection |
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Specifies the modularity tolerance value for community detection |
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Specifies the algorithm to use for connected components |
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Specifies the log level for connected components |
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Specifies the type of core to process |
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Specifies the log level for the core algorithm |
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Specifies the maximum amount of time to spend in the core algorithm |
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Specifies the log level for the cycle algorithm |
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Specifies the maximum number of cycles to return during cycle calculations |
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Specifies the maximum length for the cycles found |
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Specifies the maximum link weight for the cycles found |
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Specifies the maximum node weight for the cycles found |
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Specifies the maximum amount of time to spend calculating cycles |
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Specifies the minimum length for the cycles found |
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Specifies the minimum link weight for the cycles found |
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Specifies the minimum node weight for the cycles found |
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Specifies the mode for the cycle calculations |
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Specifies the output data set for cycles |
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Specifies the algebraic type of eigenvalues to calculate |
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Specifies the log level for eigenvector calculations |
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Specifies the maximum number of iterations for eigenvector calculation |
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Specifies the number of eigenvectors to calculate |
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Specifies the output data set for one or more eigenvectors |
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Specifies the data set variable names for the linear assignment identifiers |
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Specifies the log level for the linear assignment algorithm |
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Specifies the output data set for linear assignment |
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Specifies the data set variable names for costs (or weights) |
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Specifies the iteration log frequency |
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Specifies the log level for the minimum-cost network flow algorithm |
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Specifies the maximum amount of time to spend calculating the optimal flow |
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Specifies the log level for the minimum-cut algorithm |
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Specifies the maximum number of cuts to return from the algorithm |
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Specifies the maximum weight of the cuts to return from the algorithm |
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Specifies the output data set for minimum cut |
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Specifies the log level for the minimum spanning tree algorithm |
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Specifies the output data set for minimum spanning tree |
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Decomposes the calculations for reach by cluster (or subgraph) |
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Calculates the directed reach counts |
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Treats each node as a source in reach calculations |
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Ignores the source node in reach counts |
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Specifies the maximum number of links to allow in the reach calculations |
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Specifies the iteration log frequency (seconds) |
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Specifies the log level for reach calculations |
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Specifies the output data set for reach counts |
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Specifies the output data set for reach counts (limit=1) |
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Specifies the output data set for reach counts (limit=2) |
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Specifies the output data set for reach links |
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Specifies the output data set for reach nodes |
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Specifies the iteration log frequency (nodes) |
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Specifies the log level for shortest paths |
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Specifies the output data set for shortest paths |
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Specifies the output data set for shortest path summaries |
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Specifies the type of output for shortest paths results |
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Specifies the sink node for shortest paths calculations |
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Specifies the source node for shortest paths calculations |
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Specifies whether to use weights in calculating shortest paths |
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Specifies the data set variable name for the auxiliary link weights |
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Calculates information about biconnected components |
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Decomposes the calculations for summary by cluster (or subgraph) |
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Calculates information about connected components |
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Calculates the approximate diameter and chooses the weight type |
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Specifies the iteration log frequency (nodes) |
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Specifies the iteration log frequency (seconds) |
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Specifies the log level for summary calculations |
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Specifies the output data set for summary results |
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Calculates information about shortest paths and chooses the weight type |
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Specifies the subgraph node size to run separately |
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Specifies the log level for transitive closure |
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Specifies the output data set for transitive closure results |
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Specifies the stopping criterion based on the absolute objective gap |
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Specifies the level of conflict search |
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Specifies the cutoff value for branch-and-bound node removal |
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Specifies the overall cut strategy level |
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Emphasizes feasibility or optimality |
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Specifies the initial and primal heuristics level |
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Specifies the maximum allowed difference between an integer variable’s value and an integer |
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Specifies the log level for the traveling salesman algorithm |
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Specifies the maximum number of branch-and-bound nodes to be processed |
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Specifies the maximum number of solutions to be found |
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Specifies the maximum amount of time to spend in the algorithm |
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Specifies whether to use a mixed integer linear programming solver |
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Specifies the branch-and-bound node selection strategy |
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Specifies the output data set for traveling salesman |
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Specifies the probing level |
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Specifies the stopping criterion that is based on relative objective gap |
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Specifies the number of simplex iterations to be performed on each variable in the strong branching strategy |
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Specifies the number of candidates for the strong branching strategy |
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Specifies the stopping criterion based on the target objective value |
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Specifies the rule for selecting branching variable |
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For more information about the options available for the PERFORMANCE statement, see the section PERFORMANCE Statement.
Table 1.3 lists the valid input formats, GRAPH_DIRECTION= values, and GRAPH_INTERNAL_FORMAT= values for each statement in the OPTGRAPH procedure.
Table 1.3: Supported Input Formats and Graph Types by Statement
|
Input Format |
DIRECTION |
INTERNAL_FORMAT |
||||
|---|---|---|---|---|---|---|
|
Statement |
Graph |
Matrix |
UNDIRECTED |
DIRECTED |
THIN |
FULL |
|
X |
X |
X |
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|
X |
X |
X |
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X |
X |
X |
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X |
X |
X |
X |
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DEGREE= , |
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X |
X |
X |
X |
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X |
X |
X |
X |
X |
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X |
X |
X |
X |
X |
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DEGREE= , |
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|
X |
X |
X |
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LOUVAIN, LABEL_PROP |
X |
X |
X |
X |
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PARALLEL_LABEL_PROP |
X |
X |
X |
X |
X |
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DFS |
X |
X |
X |
X |
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UNION_FIND |
X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
X |
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X |
X |
X |
X |
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X |
X |
X |
X |
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Table 1.4 indicates for each algorithm statement in the OPTGRAPH procedure which output data set options you can specify and whether the algorithm populates the data sets specified in the OUT_NODES= and OUT_LINKS= options in the PROC OPTGRAPH statement.
Table 1.4: Output Options by Statement
|
Statement |
OUT_NODES |
OUT_LINKS |
Algorithm Statement Options |
|---|---|---|---|
|
X |
X |
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AUTH=, CLOSE=, |
X |
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CLUSTERING_COEF, |
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DEGREE=, EIGEN=, HUB=, |
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INFLUENCE= |
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BETWEEN= |
X |
X |
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ALGORITHM= |
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LOUVAIN, LABEL_PROP, |
X |
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PARALLEL_LABEL_PROP |
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|
X |
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|
X |
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|
X |
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|
X |
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BY_CLUSTER |
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BY_CLUSTER and EACH_SOURCE |
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|
X |
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X |