Find the Density of a Graph
gden computes the density of the graphs indicated by
g in collection
dat, adjusting for the type of graph in question.
one or more input graphs.
integer indicating the index of the graphs for which the density is to be calculated (or a vector thereof). If
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops.
string indicating the type of graph being evaluated. "digraph" indicates that edges should be interpreted as directed; "graph" indicates that edges are undirected.
logical; should edge values be ignored when calculating density?
The density of a graph is here taken to be the sum of tie values divided by the number of possible ties (i.e., an unbiased estimator of the graph mean); hence, the result is interpretable for valued graphs as the mean tie value when
ignore.eval==FALSE. The number of possible ties is determined by the graph type (and by
diag) in the usual fashion.
Where missing data is present, it is removed prior to calculation. The density/graph mean is thus taken relative to the observed portion of the graph.
The graph density
Carter T. Butts firstname.lastname@example.org
Wasserman, S., and Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
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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
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- closeness: Compute the Closeness Centrality Scores of Network Positions
- coleman: Coleman's High School Friendship Data
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- components: Find the Number of (Maximal) Components Within a Given Graph
- component.size.byvertex: Get Component Sizes, by Vertex
- connectedness: Compute Graph Connectedness Scores
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- 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
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- 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