## ----echo=FALSE, results="hide", message=FALSE, warning=FALSE-----------------
knitr::opts_chunk$set(
  echo = TRUE, message = FALSE, tidy = TRUE, warning = FALSE,
  fig.width = 8, fig.height = 6, fig.align = "center"
)

## -----------------------------------------------------------------------------
library(bitriad)

## -----------------------------------------------------------------------------
data(women_clique)
as_incidence_matrix(women_clique)

## ----echo=FALSE, fig.height=5-------------------------------------------------
women_clique <- prettify_an(women_clique)
V(women_clique)$label <- c(LETTERS[1:5], 1:5)
V(women_clique)$label.color <- "white"
set.seed(77)
plot(
  women_clique,
  layout = layout_with_fr(women_clique, niter = 200)
)

## -----------------------------------------------------------------------------
women_clique_proj <- actor_projection(women_clique)
( tc <- triad_census(women_clique_proj) )

## -----------------------------------------------------------------------------
( antc <- triad_census(women_clique) )

## -----------------------------------------------------------------------------
antc_proj <- project_census(antc)
antc_proj$binary

## -----------------------------------------------------------------------------
cbind(
  tc,
  antc_proj$simple,
  project_census(antc_proj$binary)$simple
)

## -----------------------------------------------------------------------------
( C <- unname(3 * tc[4] / (tc[3] + 3 * tc[4])) )

## -----------------------------------------------------------------------------
( C_vec <- c(
  C = triad_closure_from_census(antc, scheme = "full", measure = "classical"),
  OpsahlC = triad_closure_from_census(antc, scheme = "full", measure = "opsahl"),
  exclC = triad_closure_from_census(antc, scheme = "full", measure = "exclusive")
) )

## -----------------------------------------------------------------------------
stc <- antc_proj$binary
3 * sum(stc[4, ]) / (sum(stc[3, ]) + 3 * sum(stc[4, ]))

## -----------------------------------------------------------------------------
C_local <- transitivity(women_clique_proj, type = "local")
names(C_local) <- V(women_clique_proj)$name
C_local

## -----------------------------------------------------------------------------
( exclWedges <- triad_closure_exclusive(women_clique, type = "raw") )

## -----------------------------------------------------------------------------
sum(exclWedges[, 2]) / sum(exclWedges[, 1])  # global
exclWedges[, 2] / exclWedges[, 1]            # local

## -----------------------------------------------------------------------------
C_local_dat <- cbind(
  C = C_local,
  OpsahlC = triad_closure_opsahl(women_clique, type = "local"),
  exclC = triad_closure_exclusive(women_clique, type = "local")
)
rownames(C_local_dat) <- V(women_clique_proj)$name
C_local_dat

## ----fig.height=5-------------------------------------------------------------
ddc <- data.frame(
  k = degree(women_clique_proj),
  C = transitivity(women_clique_proj, type = "local")
)
print(ddc)
plot(
  aggregate(ddc$C, by = list(ddc$k), FUN = mean), pch = 19, type = "b",
  main = "Degree-dependent local clustering",
  xlab = "Degree", ylab = "Mean conditional local clustering coefficient"
)

## ----fig.height=6-------------------------------------------------------------
data(women_group)
women_group <- prettify_an(women_group)
V(women_group)$label <- substr(
  V(women_group)$name, 1,
  ifelse(V(women_group)$type, 5, 2)
)
V(women_group)$label.color <- "white"
set.seed(2)
plot(women_group, layout = layout_as_bipartite(women_group))

## ----fig.height=5-------------------------------------------------------------
women_group_proj <- actor_projection(women_group)
( ddc2 <- data.frame(
  k = degree(women_group_proj),
  C = transitivity(women_group_proj, type = "local")
) )
plot(
  aggregate(ddc2$C, by = list(k = ddc2$k), FUN = mean),
  pch = 19, type = "b",
  main = "Degree-dependent local clustering",
  xlab = "Degree", ylab = "Mean conditional local clustering coefficient"
)

## ----fig.height=5-------------------------------------------------------------
women_group_wedges <- triad_closure_opsahl(women_group, type = "raw")
women_group_wedges <- cbind(
  women_group_wedges,
  women_group_wedges[, 2] / women_group_wedges[, 1]
)
plot(
  aggregate(
    women_group_wedges[, 3],
    by = list(women_group_wedges[, 1]),
    FUN = mean
  ),
  pch = 19, type = "b",
  main = "Wedge-dependent local clustering (Opsahl)",
  xlab = "Wedges", ylab = "Mean conditional local clustering coefficient"
)

## ----fig.height=5-------------------------------------------------------------
women_group_wedges <- triad_closure_exclusive(women_group, type = "raw")
women_group_wedges <- cbind(
  women_group_wedges,
  C = women_group_wedges[, 2] / women_group_wedges[, 1]
)
plot(
  aggregate(
    women_group_wedges[, 3],
    by = list(women_group_wedges[, 1]),
    FUN = mean
  ),
  pch = 19, type = "b",
  main = "Wedge-dependent local clustering (exclusive)",
  xlab = "Wedges", ylab = "Mean conditional local clustering coefficient"
)