net_x_homophily() returns the two-by-two table from which network-level
homophily is calculated, together with the summaries built from it.
Where net_by_heterophily() returns the EI index alone,
this returns the counts it rests on, so that the index can be interpreted
against the network's own composition.
Note that on a weighted network the two report different values.
A contingency table counts ties, so net_x_homophily() treats every tie
alike, whereas net_by_heterophily() sums tie weights and so gives more
say to stronger ties. On unweighted networks the two agree exactly.
Apply manynet::to_unweighted() first to compare them directly.
net_x_homophily(.data, attribute)A network object of class stocnet, igraph, tbl_graph, network, or similar.
Internally any of these will be coerced to an efficient implementation.
For more information on possible coercions, see e.g. manynet::as_stocnet().
Name of a nodal attribute, mark, measure, or membership vector.
A network_motif named numeric vector or sometimes a data frame with
one row and a column for each motif type,
giving the count of each motif in the network.
This is printed as a tibble to avoid greedy printing of long vectors.
The EI index depends on how large the categories are, not only on how nodes choose between them. A network split into two equal groups will have a lower EI than one in which a small minority is surrounded by a large majority, even if nodes in both are equally indifferent to category.
ExpectedEI gives the EI that would be observed if ties were distributed
at random across all possible pairs, holding category sizes fixed.
Comparing EI against it separates the network's mixing from its
composition: an EI above the expected value indicates more crossing of
category boundaries than chance alone would produce, and one below it
indicates less.
Krackhardt, David, and Robert N. Stern. 1988. "Informal Networks and Organizational Crises: An Experimental Simulation". Social Psychology Quarterly 51(2): 123-140. doi:10.2307/2786835
Other motifs:
motif_brokerage_net,
motif_brokerage_node,
motif_clique,
motif_composition,
motif_exposure,
motif_hazard,
motif_hierarchy,
motif_net,
motif_node,
motif_path,
motif_periods
Other diversity:
measure_assort_net,
measure_assort_node,
measure_diverse_net,
measure_diverse_node,
motif_composition
net_x_homophily(ison_networkers, "Discipline")
#> # A tibble: 1 × 8
#> TieSame TieDiff NoTieSame NoTieDiff PctSame EI ExpectedEI YulesQ
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 144 296 194 358 0.327 0.345 0.319 -0.0539