Analyzing Partial Rankings in Networks
Quantification of (indirect) relations
Approximation of expected ranks
Approximation of relative rank probabilities
Extract probabilities from netrankr_full object
Comparable pairs in a partial order
Count occurrences of pairs in rankings
Partial ranking as directed graph
Probabilistic centrality rankings
Rankings that extend a partial ranking
Hyperbolic (centrality) index
Incomparable pairs in a partial order
Centrality Index Builder
Indirect relations in a network
Check preservation
Majorization gap
Estimate rank probabilities with Markov Chains
Neighborhood-inclusion preorder
netrankr: An R package for centrality and partial rankings in networks
Plot rank intervals
Plot netrankr_full object
plot netrankr_interval objects
Plot netrankr_mcmc object
Generalized Dominance Relations
Print netrankr_full object to terminal
Print netrankr_interval object to terminal
Print netrankr_mcmc object to terminal
Rank interval of nodes
Spectral gap of a graph
Summary of a netrankr_full object
Impact on closeness when a node is removed
Error and attack tolerance of complex networks
Impact on connectivity when a node is removed
Impact on farness when a node is removed
Random threshold graphs
Transform indirect relations
Transitive Reduction
Implements methods for centrality related analyses of networks. While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.
Useful links