heterogeneity  R Documentation 
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()
measures the number of unique categories
in a network attribute.
node_richness()
measures the number of unique categories
of an attribute to which each node is connected.
network_diversity()
measures the heterogeneity of ties across a network
or within clusters by node attributes.
node_diversity()
measures the heterogeneity of each node's
local neighbourhood.
network_heterophily()
measures how embedded nodes in the network
are within groups of nodes with the same attribute.
node_heterophily()
measures each node's embeddedness within groups
of nodes with the same attribute.
network_assortativity()
measures the degree assortativity in a network.
network_spatial()
measures the spatial association/autocorrelation (
global Moran's I) in a network.
network_richness(.data, attribute)
node_richness(.data, attribute)
network_diversity(.data, attribute, clusters = NULL)
node_diversity(.data, attribute)
network_heterophily(.data, attribute)
node_heterophily(.data, attribute)
network_assortativity(.data)
network_spatial(.data, attribute)
.data 
An object of a

attribute 
Name of a nodal attribute or membership vector to use as categories for the diversity measure. 
clusters 
A nodal cluster membership vector or name of a vertex attribute. 
Blau's index (1977) uses a formula known also in other disciplines by other names (GiniSimpson Index, Gini impurity, Gini's diversity index, GibbsMartin 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 EI 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), 123140.
Moran, Patrick Alfred Pierce. 1950. "Notes on Continuous Stochastic Phenomena". Biometrika 37(1): 1723. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/2332142")}
Other measures:
between_centrality
,
close_centrality
,
closure
,
cohesion()
,
degree_centrality
,
eigenv_centrality
,
features
,
hierarchy
,
holes
,
net_diffusion
,
node_diffusion
,
periods
network_richness(mpn_bristol)
node_richness(mpn_bristol, "type")
marvel_friends < manynet::to_unsigned(manynet::ison_marvel_relationships, "positive")
network_diversity(marvel_friends, "Gender")
network_diversity(marvel_friends, "Attractive")
network_diversity(marvel_friends, "Gender", "Rich")
node_diversity(marvel_friends, "Gender")
node_diversity(marvel_friends, "Attractive")
network_heterophily(marvel_friends, "Gender")
network_heterophily(marvel_friends, "Attractive")
node_heterophily(marvel_friends, "Gender")
node_heterophily(marvel_friends, "Attractive")
network_assortativity(mpn_elite_mex)
network_spatial(ison_lawfirm, "age")
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