Vectorization of Adjacency Matrices
gvectorize takes an input graph set and converts it into a corresponding number of vectors by row concatenation.
one or more input graphs.
“digraph” if data is taken to be directed, else “graph”.
boolean indicating whether diagonal entries (loops) are taken to contain meaningful data.
The output of
gvectorize is a matrix in which each column corresponds to an input graph, and each row corresponds to an edge. The columns of the output matrix are formed by simple row-concatenation of the original adjacency matrices, possibly after removing cells which are not meaningful (if
censor.as.na==FALSE). This is useful when preprocessing edge sets for use with
glm or the like.
An nxk matrix, where n is the number of arcs and k is the number of graphs; if
censor.as.na==FALSE, n will be reflect the relevant number of uncensored arcs.
Carter T. Butts firstname.lastname@example.org
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- add.isolates: Add Isolates to a Graph
- bbnam: Butts' (Hierarchical) Bayesian Network Accuracy Model
- bbnam.bf: Estimate Bayes Factors for the bbnam
- betweenness: Compute the Betweenness Centrality Scores of Network...
- bicomponent.dist: Calculate the Bicomponents of a Graph
- blockmodel: Generate Blockmodels Based on Partitions of Network Positions
- blockmodel.expand: Generate a Graph (or Stack) from a Given Blockmodel Using...
- bn: Fit a Biased Net Model
- bonpow: Find Bonacich Power Centrality Scores of Network Positions
- brokerage: Perform a Gould-Fernandez Brokerage Analysis
- centralgraph: Find the Central Graph of a Labeled Graph Stack
- centralization: Find the Centralization of a Given Network, for Some Measure...
- clique.census: Compute Cycle Census Information
- closeness: Compute the Closeness Centrality Scores of Network Positions
- coleman: Coleman's High School Friendship Data
- component.dist: Calculate the Component Size Distribution of a Graph
- components: Find the Number of (Maximal) Components Within a Given Graph
- component.size.byvertex: Get Component Sizes, by Vertex
- connectedness: Compute Graph Connectedness Scores
- consensus: Estimate a Consensus Structure from Multiple Observations
- cugtest: Perform Conditional Uniform Graph (CUG) Hypothesis Tests for...
- cug.test: Univariate Conditional Uniform Graph Tests
- cutpoints: Identify the Cutpoints of a Graph or Digraph
- degree: Compute the Degree Centrality Scores of Network Positions
- diag.remove: Remove the Diagonals of Adjacency Matrices in a Graph Stack
- dyad.census: Compute a Holland and Leinhardt MAN Dyad Census
- efficiency: Compute Graph Efficiency Scores
- ego.extract: Extract Egocentric Networks from Complete Network Data
- equiv.clust: Find Clusters of Positions Based on an Equivalence Relation
- eval.edgeperturbation: Compute the Effects of Single-Edge Perturbations on...
- evcent: Find Eigenvector Centrality Scores of Network Positions
- event2dichot: Convert an Observed Event Matrix to a Dichotomous matrix
- flowbet: Calculate Flow Betweenness Scores of Network Positions
- gapply: Apply Functions Over Vertex Neighborhoods
- gclust.boxstats: Plot Statistics Associated with Graph Clusters
- gclust.centralgraph: Get Central Graphs Associated with Graph Clusters
- gcor: Find the (Product-Moment) Correlation Between Two or More...
- gcov: Find the Covariance(s) Between Two or More Labeled Graphs
- gden: Find the Density of a Graph
- gdist.plotdiff: Plot Differences in Graph-level Statistics Against...
- gdist.plotstats: Plot Various Graph Statistics Over a Network MDS
- geodist: Fund the Numbers and Lengths of Geodesics Among Nodes in a...
- gilschmidt: Compute the Gil-Schmidt Power Index
- gliop: Return a Binary Operation on GLI Values Computed on Two...
- gplot: Two-Dimensional Visualization of Graphs
- gplot3d: Three-Dimensional Visualization of Graphs
- gplot3d.arrow: Add Arrows a Three-Dimensional Plot
- gplot3d.layout: Vertex Layout Functions for gplot3d
- gplot3d.loop: Add Loops to a Three-Dimensional Plot
- gplot.arrow: Add Arrows or Segments to a Plot
- gplot.layout: Vertex Layout Functions for gplot
- gplot.loop: Add Loops to a Plot
- gplot.target: Display a Graph in Target Diagram Form
- gplot.vertex: Add Vertices to a Plot
- graphcent: Compute the (Harary) Graph Centrality Scores of Network...
- grecip: Compute the Reciprocity of an Input Graph or Graph Stack
- gscor: Find the Structural Correlations Between Two or More Graphs
- gscov: Find the Structural Covariance(s) Between Two or More Graphs
- gt: Transpose an Input Graph
- gtrans: Compute the Transitivity of an Input Graph or Graph Stack
- gvectorize: Vectorization of Adjacency Matrices
- hdist: Find the Hamming Distances Between Two or More Graphs
- hierarchy: Compute Graph Hierarchy Scores
- infocent: Find Information Centrality Scores of Network Positions
- interval.graph: Convert Spell Data to Interval Graphs
- is.connected: Is a Given Graph Connected?
- is.isolate: Is Ego an Isolate?
- isolates: List the Isolates in a Graph or Graph Stack
- kcores: Compute the k-Core Structure of a Graph
- lab.optimize: Optimize a Bivariate Graph Statistic Across a Set of...
- lnam: Fit a Linear Network Autocorrelation Model
- loadcent: Compute the Load Centrality Scores of Network Positions
- lower.tri.remove: Remove the Lower Triangles of Adjacency Matrices in a Graph...
- lubness: Compute Graph LUBness Scores
- make.stochastic: Make a Graph Stack Row, Column, or Row-column Stochastic
- maxflow: Calculate Maximum Flows Between Vertices
- mutuality: Find the Mutuality of a Graph
- nacf: Sample Network Covariance and Correlation Functions
- neighborhood: Compute Neighborhood Structures of Specified Order
- netcancor: Canonical Correlation for Labeled Graphs
- netlm: Linear Regression for Network Data
- netlogit: Logistic Regression for Network Data
- npostpred: Take Posterior Predictive Draws for Functions of Networks
- nties: Find the Number of Possible Ties in a Given Graph or Graph...
- numperm: Get the nth Permutation Vector by Periodic Placement
- path.census: Compute Path or Cycle Census Information
- plot.bbnam: Plotting for bbnam Objects
- plot.blockmodel: Plotting for blockmodel Objects
- plot.cugtest: Plotting for cugtest Objects
- plot.equiv.clust: Plot an equiv.clust Object
- plot.lnam: Plotting for lnam Objects
- plot.qaptest: Plotting for qaptest Objects
- plot.sociomatrix: Plot Matrices Using a Color/Intensity Grid
- potscalered.mcmc: Compute Gelman and Rubin's Potential Scale Reduction Measure...
- prestige: Calculate the Vertex Prestige Scores
- print.bayes.factor: Printing for Bayes Factor Objects
- print.bbnam: Printing for bbnam Objects
- print.blockmodel: Printing for blockmodel Objects
- print.cugtest: Printing for cugtest Objects
- print.lnam: Printing for lnam Objects
- print.netcancor: Printing for netcancor Objects
- print.netlm: Printing for netlm Objects
- print.netlogit: Printing for netlogit Objects
- print.qaptest: Printing for qaptest Objects
- print.summary.bayes.factor: Printing for summary.bayes.factor Objects
- print.summary.bbnam: Printing for summary.bbnam Objects
- print.summary.blockmodel: Printing for summary.blockmodel Objects
- print.summary.cugtest: Printing for summary.cugtest Objects
- print.summary.lnam: Printing for summary.lnam Objects
- print.summary.netcancor: Printing for summary.netcancor Objects
- print.summary.netlm: Printing for summary.netlm Objects
- print.summary.netlogit: Printing for summary.netlogit Objects
- print.summary.qaptest: Printing for summary.qaptest Objects
- pstar: Fit a p*/ERG Model Using a Logistic Approximation
- qaptest: Perform Quadratic Assignment Procedure (QAP) Hypothesis Tests...
- reachability: Find the Reachability Matrix of a Graph
- read.dot: Read Graphviz DOT Files
- read.nos: Read (N)eo-(O)rg(S)tat Input Files
- redist: Find a Matrix of Distances Between Positions Based on Regular...
- rgbn: Draw from a Skvoretz-Fararo Biased Net Process
- rgnm: Draw Density-Conditioned Random Graphs
- rgnmix: Draw Mixing-Conditioned Random Graphs
- rgraph: Generate Bernoulli Random Graphs
- rguman: Draw Dyad Census-Conditioned Random Graphs
- rgws: Draw From the Watts-Strogatz Rewiring Model
- rmperm: Randomly Permute the Rows and Columns of an Input Matrix
- rperm: Draw a Random Permutation Vector with Exchangeability...
- sdmat: Estimate the Structural Distance Matrix for a Graph Stack
- sedist: Find a Matrix of Distances Between Positions Based on...
- sna: Tools for Social Network Analysis
- sna-coercion: sna Coercion Functions
- sna-defunct: Defunct sna Objects
- sna-deprecated: Deprecated Functions in sna Package
- sna-internal: Internal sna Functions
- sna.operators: Graphical Operators
- sr2css: Convert a Row-wise Self-Report Matrix to a CSS Matrix with...
- stackcount: How Many Graphs are in a Graph Stack?
- stresscent: Compute the Stress Centrality Scores of Network Positions
- structdist: Find the Structural Distances Between Two or More Graphs
- structure.statistics: Compute Network Structure Statistics
- summary.bayes.factor: Detailed Summaries of Bayes Factor Objects
- summary.bbnam: Detailed Summaries of bbnam Objects
- summary.blockmodel: Detailed Summaries of blockmodel Objects
- summary.cugtest: Detailed Summaries of cugtest Objects
- summary.lnam: Detailed Summaries of lnam Objects
- summary.netcancor: Detailed Summaries of netcancor Objects
- summary.netlm: Detailed Summaries of netlm Objects
- summary.netlogit: Detailed Summaries of netlogit Objects
- summary.qaptest: Detailed Summaries of qaptest Objects
- symmetrize: Symmetrize an Adjacency Matrix
- triad.census: Compute the Davis and Leinhardt Triad Census
- triad.classify: Compute the Davis and Leinhardt Classification of a Given...
- upper.tri.remove: Remove the Upper Triangles of Adjacency Matrices in a Graph...
- write.dl: Write Output Graphs in DL Format
- write.nos: Write Output Graphs in (N)eo-(O)rg(S)tat Format