mark_core: Core-periphery clustering algorithms

In manynet: Many Ways to Make, Modify, Map, Mark, and Measure Myriad Networks

Core-periphery clustering algorithms

Description

These functions identify nodes belonging to (some level of) the core of a network:

• node_is_core() assigns nodes to either the core or periphery.

• node_coreness() assigns nodes to their level of k-coreness.

Usage

node_is_universal(.data)

node_is_core(.data, method = c("degree", "eigenvector"))

node_coreness(.data)

Arguments

.data	An object of a manynet-consistent class: matrix (adjacency or incidence) from {base} R edgelist, a data frame from {base} R or tibble from {tibble} igraph, from the {igraph} package network, from the {network} package tbl_graph, from the {tidygraph} package
method	Which method to use to identify cores and periphery. By default this is "degree", which relies on the heuristic that high degree nodes are more likely to be in the core. An alternative is "eigenvector", which instead begins with high eigenvector nodes. Other methods, such as a genetic algorithm, CONCOR, and Rombach-Porter, can be added if there is interest.

Universal/dominating node

A universal node is adjacent to all other nodes in the network. It is also sometimes called the dominating vertex because it represents a one-element dominating set. A network with a universal node is called a cone, and its universal node is called the apex of the cone. A classic example of a cone is a star graph, but friendship, wheel, and threshold graphs are also cones.

Core-periphery

This function is used to identify which nodes should belong to the core, and which to the periphery. It seeks to minimize the following quantity:

Z(S_1) = \sum_{(i<j)\in S_1} \textbf{I}_{\{A_{ij}=0\}} + \sum_{(i<j)\notin S_1} \textbf{I}_{\{A_{ij}=1\}}

where nodes \{i,j,...,n\} are ordered in descending degree, A is the adjacency matrix, and the indicator function is 1 if the predicate is true or 0 otherwise. Note that minimising this quantity maximises density in the core block and minimises density in the periphery block; it ignores ties between these blocks.

References

On core-periphery partitioning

Borgatti, Stephen P., & Everett, Martin G. 1999. "Models of core /periphery structures". Social Networks, 21, 375–395. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0378-8733(99)00019-2")}

Lip, Sean Z. W. 2011. “A Fast Algorithm for the Discrete Core/Periphery Bipartitioning Problem.” \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1102.5511")}

See Also

Other memberships: member_brokerage, member_cliques, member_community_hier, member_community_non, member_components, member_equivalence

Examples

  node_is_universal(create_star(11))
node_is_core(ison_adolescents)
#ison_adolescents %>% 
#   mutate(corep = node_is_core()) %>% 
#   graphr(node_color = "corep")
node_coreness(ison_adolescents)

manynet documentation built on June 23, 2025, 9:07 a.m.