## ----libs, warning=FALSE,message=FALSE----------------------------------------
library(igraph)
library(netrankr)
library(magrittr)

## ----plot,fig.height=5,fig.width=5,fig.align='center'-------------------------
data("florentine_m")
# Delete Pucci family (isolated)
florentine_m <- delete_vertices(florentine_m, which(degree(florentine_m) == 0))

# plot the graph (label size proportional to wealth)
set.seed(111)
plot(florentine_m,
  vertex.label.cex = V(florentine_m)$wealth * 0.01,
  vertex.label.color = "black",
  vertex.color = "white",
  vertex.frame.color = "gray"
)

## ----cent---------------------------------------------------------------------
cent.df <- data.frame(
  degree = degree(florentine_m),
  betweenness = betweenness(florentine_m),
  closeness = closeness(florentine_m),
  eigenvector = eigen_centrality(florentine_m)$vector,
  subgraph = subgraph_centrality(florentine_m)
)

# most central family according to the 5 indices
V(florentine_m)$name[apply(cent.df, 2, which.max)]

## ----cent_new-----------------------------------------------------------------
# odd subgraph centrality
sc_odd <- florentine_m %>%
  indirect_relations(type = "walks", FUN = walks_exp_odd) %>%
  aggregate_positions(type = "self")

# family with highest score
V(florentine_m)$name[which.max(sc_odd)]

## ----ni-----------------------------------------------------------------------
P <- neighborhood_inclusion(florentine_m)

## ----ni_comp------------------------------------------------------------------
comparable_pairs(P)

## ----dom_graph,fig.height=5,fig.width=5,fig.align='center'--------------------
d <- dominance_graph(P)
V(d)$name <- V(florentine_m)$name
set.seed(113)
plot(d,
  vertex.label.color = "black",
  vertex.color = "white",
  vertex.frame.color = "gray",
  edge.arrow.size = 0.5
)

## ----rk_inter,fig.align='center',fig.width=5,fig.height=5---------------------
plot(rank_intervals(P))

## ----probs--------------------------------------------------------------------
res <- exact_rank_prob(P)

## ----likely_most_central------------------------------------------------------
top_rank_prob <- res$rank.prob[, 15]
names(top_rank_prob) <- V(florentine_m)$name
round(top_rank_prob[top_rank_prob > 0.1], 3)

## ----medici_strozzi,eval=TRUE-------------------------------------------------
id_strozzi <- which(V(florentine_m)$name == "Strozzi")
id_medici <- which(V(florentine_m)$name == "Medici")
res$relative.rank[id_strozzi, id_medici]

## ----exp_rank,echo=FALSE------------------------------------------------------
tab <- data.frame(Name = V(florentine_m)$name, Expected = round(res$expected.rank, 2))
tab <- tab[order(tab[, 2], decreasing = TRUE), ]
knitr::kable(tab, row.names = F)

## ----closeness_vs_wealth------------------------------------------------------
# Closeness
c_C <- florentine_m %>%
  indirect_relations(type = "dist_sp") %>%
  aggregate_positions(type = "invsum")

cor(c_C, V(florentine_m)$wealth, method = "kendall")

## ----harmclos_wealth----------------------------------------------------------
# harmonic closeness
c_HC <- florentine_m %>%
  indirect_relations(type = "dist_sp", FUN = dist_inv) %>%
  aggregate_positions(type = "sum")
# residual closeness (Dangalchev,2006)
c_RC <- florentine_m %>%
  indirect_relations(type = "dist_sp", FUN = dist_2pow) %>%
  aggregate_positions(type = "sum")

# integration centrality (Valente & Foreman, 1998)
dist_integration <- function(x) {
  x <- 1 - (x - 1) / max(x)
}
c_IN <- florentine_m %>%
  indirect_relations(type = "dist_sp", FUN = dist_integration) %>%
  aggregate_positions(type = "sum")

c(
  cor(c_HC, V(florentine_m)$wealth, method = "kendall"),
  cor(c_RC, V(florentine_m)$wealth, method = "kendall"),
  cor(c_IN, V(florentine_m)$wealth, method = "kendall")
)

## ----distalpha_wealth,warning=FALSE-------------------------------------------
# generalized closeness (Agneessens et al.,2017) (alpha>0) sum(dist^-alpha)
alpha <- c(seq(0.01, 0.99, 0.01), seq(1, 10, 0.1))
scores <-
  sapply(alpha, function(x) {
    florentine_m %>%
      indirect_relations(type = "dist_sp", FUN = dist_dpow, alpha = x) %>%
      aggregate_positions(type = "sum")
  })
cors_gc <- apply(
  scores, 2,
  function(x) cor(x, V(florentine_m)$wealth, method = "kendall")
)

res_gc <- c(max(cors_gc), alpha[which.max(cors_gc)])

# decay centrality (Jackson, 2010) (alpha in [0,1]) sum(alpha^dist)
alpha <- seq(0.01, 0.99, 0.01)
scores <-
  sapply(alpha, function(x) {
    florentine_m %>%
      indirect_relations(type = "dist_sp", FUN = dist_powd, alpha = x) %>%
      aggregate_positions(type = "sum")
  })
cors_dc <- apply(
  scores, 2,
  function(x) cor(x, V(florentine_m)$wealth, method = "kendall")
)

res_dc <- c(max(cors_dc), alpha[which.max(cors_dc)])

## ----pos_dom_hetero-----------------------------------------------------------
D <- florentine_m %>%
  indirect_relations(type = "dist_sp") %>%
  positional_dominance(benefit = F)

comparable_pairs(D)

## ----pos_dom_homo-------------------------------------------------------------
D <- florentine_m %>%
  indirect_relations(type = "dist_sp") %>%
  positional_dominance(benefit = F, map = T)

comparable_pairs(D)

## ----plot dist_dom,fig.align="center",fig.height=5,fig.width=5,echo=F---------
d <- dominance_graph(D)
V(d)$name <- V(florentine_m)$name
x <- V(florentine_m)$wealth
x[9] <- x[9] - 50
x[14] <- x[14] - 50
y <- colSums(D)
el <- get.edgelist(d, names = F)
E(d)$color <- "gray"
col <- apply(el, 1, function(x) V(florentine_m)$wealth[x[1]] > V(florentine_m)$wealth[x[2]])
# wrong:41
E(d)$color[col] <- "indianred"

plot(d,
  layout = cbind(x, y),
  vertex.label.cex = 0.75,
  vertex.label.color = "black",
  vertex.color = "white",
  vertex.frame.color = "gray",
  edge.arrow.size = 0.4
)

## ----rk_inter_dist,fig.align='center',fig.width=5,fig.height=5----------------
plot(rank_intervals(D))

## ----dist_probs---------------------------------------------------------------
res <- exact_rank_prob(D)

## ----dist_probs_lat-----------------------------------------------------------
res <- exact_rank_prob(D, only.results = FALSE)

## ----all_rankings-------------------------------------------------------------
all_ranks <- get_rankings(res)
dim(all_ranks)

## ----all_cors-----------------------------------------------------------------
dist_cor <- apply(
  all_ranks, 2,
  function(x) cor(V(florentine_m)$wealth, x, method = "kendall")
)
c(max_cor = max(dist_cor), mean_cor = mean(dist_cor))

## ----cor_deg------------------------------------------------------------------
cor(degree(florentine_m), V(florentine_m)$wealth, method = "kendall")