View source: R/neighborhood.inclusion.R

neighborhood_inclusion | R Documentation |

Calculates the neighborhood-inclusion preorder of an undirected graph.

neighborhood_inclusion(g, sparse = FALSE)

`g` |
An igraph object |

`sparse` |
Logical scalar, whether to create a sparse matrix |

Neighborhood-inclusion is defined as

*N(u)\subseteq N[v]*

where *N(u)* is the neighborhood of *u* and *N[v]=N(v)\cup \lbrace v\rbrace* is the closed neighborhood of *v*.
*N(u) \subseteq N[v]* implies that *c(u) ≤q c(v)*,
where *c* is a centrality index based on a specific path algebra. Indices
falling into this category are closeness (and variants), betweenness
(and variants) as well as many walk-based indices (eigenvector and subgraph
centrality, total communicability,...).

The neighborhood-inclusion preorder of `g`

as matrix object. `P[u,v]=1`

if *N(u)\subseteq N[v]*

David Schoch

Schoch, D. and Brandes, U., 2016. Re-conceptualizing centrality in social networks.
*European Journal of Applied Mathematics* 27(6), 971-985.

Brandes, U. Heine, M., Müller, J. and Ortmann, M., 2017.
Positional Dominance: Concepts and Algorithms.
*Conference on Algorithms and Discrete Applied Mathematics*, 60-71.

positional_dominance, exact_rank_prob

library(igraph) # the neighborhood inclusion preorder of a star graph is complete g <- graph.star(5, "undirected") P <- neighborhood_inclusion(g) comparable_pairs(P) # the same holds for threshold graphs tg <- threshold_graph(50, 0.1) P <- neighborhood_inclusion(tg) comparable_pairs(P) # standard centrality indices preserve neighborhood-inclusion data("dbces11") P <- neighborhood_inclusion(dbces11) is_preserved(P, degree(dbces11)) is_preserved(P, closeness(dbces11)) is_preserved(P, betweenness(dbces11))

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