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
Size Limitations
PROC OPTGRAPH can handle any graph whose number of nodes and links are less than or equal to 2,147,483,647 (the maximum that can be represented by a 32-bit integer). This maximum also applies to 64-bit systems. For graphs of two billion nodes (or links), memory limitations also become a limiting factor. For example, see the discussion of memory requirements for the community detection algorithm in the section Memory Requirement
If the data from your problem require a graph with more than two billion nodes (or links), there is typically a heuristic way to break the network into smaller networks based on problem-specific attributes. Then, using DATA steps, you can process each of the smaller networks iteratively through repeated calls to PROC OPTGRAPH. By using DATA steps, you can also often work around memory limitations, because the full graph exists only on the disk and never resides in-memory.