These functions offer ways to summarise the heterogeneity of an attribute across a network, within groups of a network, or the distribution of ties across this attribute.
network_richness(object, attribute)
node_richness(object, attribute)
network_diversity(object, attribute, clusters = NULL)
node_diversity(object, attribute)
network_homophily(object, attribute)
node_homophily(object, attribute)
network_assortativity(object)An object of a migraph-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
Name of a nodal attribute or membership vector to use as categories for the diversity measure.
A nodal cluster membership vector or name of a vertex attribute.
network_richness(): Calculates the number of unique categories
in a network attribute.
node_richness(): Calculates the number of unique categories
of an attribute to which each node is connected.
network_diversity(): Calculates the heterogeneity of ties across a network or
within clusters by node attributes.
node_diversity(): Calculates the heterogeneity of each node's
local neighbourhood.
network_homophily(): Calculates how embedded nodes in the network
are within groups of nodes with the same attribute
node_homophily(): Calculates each node's embeddedness within groups
of nodes with the same attribute
network_assortativity(): Calculates the degree assortativity in a graph.
Blau's index (1977) uses a formula known also in other disciplines by other names (Gini-Simpson Index, Gini impurity, Gini's diversity index, Gibbs-Martin index, and probability of interspecific encounter (PIE)): $$1 - \sum\limits_{i = 1}^k {p_i^2 }$$, where \(p_i\) is the proportion of group members in \(i\)th category and \(k\) is the number of categories for an attribute of interest. This index can be interpreted as the probability that two members randomly selected from a group would be from different categories. This index finds its minimum value (0) when there is no variety, i.e. when all individuals are classified in the same category. The maximum value depends on the number of categories and whether nodes can be evenly distributed across categories.
Given a partition of a network into a number of mutually exclusive groups then The E-I index is the number of ties between (or external) nodes grouped in some mutually exclusive categories minus the number of ties within (or internal) these groups divided by the total number of ties. This value can range from 1 to -1, where 1 indicates ties only between categories/groups and -1 ties only within categories/groups.
Blau, Peter M. (1977). Inequality and heterogeneity. New York: Free Press.
Krackhardt, David and Robert N. Stern (1988). Informal networks and organizational crises: an experimental simulation. Social Psychology Quarterly 51(2), 123-140.
Other measures:
centralisation,
centrality,
closure,
cohesion(),
features,
holes,
tie_centrality
marvel_friends <- to_unsigned(ison_marvel_relationships, "positive")
network_diversity(marvel_friends, "Gender")
#> [1] 0.306
network_diversity(marvel_friends, "Attractive")
#> [1] 0.35
network_diversity(marvel_friends, "Gender", "Rich")
#> [1] 0.337 0.165
node_diversity(marvel_friends, "Gender")
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
#> 1 0 0.48 0 0.363 0.340 0.337 0 0 0 0.332 0.408 0.401 0.355
#> # ... with 40 more from this nodeset in the vector.
node_diversity(marvel_friends, "Attractive")
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
#> 1 0 0.32 0 0.172 0.159 0.133 0 0.375 0 0.188 0.133 0.105 0.260
#> # ... with 40 more from this nodeset in the vector.
network_homophily(marvel_friends, "Gender")
#> [1] -0.285
network_homophily(marvel_friends, "Attractive")
#> [1] -0.632
node_homophily(marvel_friends, "Gender")
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
#> 1 NaN 0.429 -1 -0.191 -0.343 -0.2 -1 -1 NaN -0.345 -0.114 -0.113
#> # ... with 41 more from this nodeset in the vector.
node_homophily(marvel_friends, "Attractive")
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
#> 1 NaN -0.143 -1 -0.649 -0.636 -0.633 -1 -0.333 NaN -0.597 -0.829
#> # ... with 42 more from this nodeset in the vector.
network_assortativity(mpn_elite_mex)
#> [1] -0.176