Week 4

rm(list = ls())

#install.packages("sna")
library(openalexR)
library(tidyverse)
require(igraph)

g <- make_graph("Zachary")
plot(g)

gmat <- as_adjacency_matrix(g, type = "both", sparse = FALSE)
gmat

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17]
##  [1,]    0    1    1    1    1    1    1    1    1     0     1     1     1     1     0     0     0
##  [2,]    1    0    1    1    0    0    0    1    0     0     0     0     0     1     0     0     0
##  [3,]    1    1    0    1    0    0    0    1    1     1     0     0     0     1     0     0     0
##  [4,]    1    1    1    0    0    0    0    1    0     0     0     0     1     1     0     0     0
##  [5,]    1    0    0    0    0    0    1    0    0     0     1     0     0     0     0     0     0
##  [6,]    1    0    0    0    0    0    1    0    0     0     1     0     0     0     0     0     1
##  [7,]    1    0    0    0    1    1    0    0    0     0     0     0     0     0     0     0     1
##  [8,]    1    1    1    1    0    0    0    0    0     0     0     0     0     0     0     0     0
##  [9,]    1    0    1    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [10,]    0    0    1    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [11,]    1    0    0    0    1    1    0    0    0     0     0     0     0     0     0     0     0
## [12,]    1    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [13,]    1    0    0    1    0    0    0    0    0     0     0     0     0     0     0     0     0
## [14,]    1    1    1    1    0    0    0    0    0     0     0     0     0     0     0     0     0
## [15,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [16,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [17,]    0    0    0    0    0    1    1    0    0     0     0     0     0     0     0     0     0
## [18,]    1    1    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [19,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [20,]    1    1    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [21,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [22,]    1    1    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [23,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [24,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [25,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [26,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [27,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [28,]    0    0    1    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [29,]    0    0    1    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [30,]    0    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [31,]    0    1    0    0    0    0    0    0    1     0     0     0     0     0     0     0     0
## [32,]    1    0    0    0    0    0    0    0    0     0     0     0     0     0     0     0     0
## [33,]    0    0    1    0    0    0    0    0    1     0     0     0     0     0     1     1     0
## [34,]    0    0    0    0    0    0    0    0    1     1     0     0     0     1     1     1     0
##       [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
##  [1,]     1     0     1     0     1     0     0     0     0     0     0     0     0     0     1
##  [2,]     1     0     1     0     1     0     0     0     0     0     0     0     0     1     0
##  [3,]     0     0     0     0     0     0     0     0     0     0     1     1     0     0     0
##  [4,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
##  [5,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
##  [6,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
##  [7,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
##  [8,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
##  [9,]     0     0     0     0     0     0     0     0     0     0     0     0     0     1     0
## [10,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [11,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [12,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [13,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [14,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [15,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [16,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [17,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [18,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [19,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [20,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [21,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [22,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [23,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [24,]     0     0     0     0     0     0     0     0     1     0     1     0     1     0     0
## [25,]     0     0     0     0     0     0     0     0     1     0     1     0     0     0     1
## [26,]     0     0     0     0     0     0     1     1     0     0     0     0     0     0     1
## [27,]     0     0     0     0     0     0     0     0     0     0     0     0     1     0     0
## [28,]     0     0     0     0     0     0     1     1     0     0     0     0     0     0     0
## [29,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     1
## [30,]     0     0     0     0     0     0     1     0     0     1     0     0     0     0     0
## [31,]     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0
## [32,]     0     0     0     0     0     0     0     1     1     0     0     1     0     0     0
## [33,]     0     1     0     1     0     1     1     0     0     0     0     0     1     1     1
## [34,]     0     1     1     1     0     1     1     0     0     1     1     1     1     1     1
##       [,33] [,34]
##  [1,]     0     0
##  [2,]     0     0
##  [3,]     1     0
##  [4,]     0     0
##  [5,]     0     0
##  [6,]     0     0
##  [7,]     0     0
##  [8,]     0     0
##  [9,]     1     1
## [10,]     0     1
## [11,]     0     0
## [12,]     0     0
## [13,]     0     0
## [14,]     0     1
## [15,]     1     1
## [16,]     1     1
## [17,]     0     0
## [18,]     0     0
## [19,]     1     1
## [20,]     0     1
## [21,]     1     1
## [22,]     0     0
## [23,]     1     1
## [24,]     1     1
## [25,]     0     0
## [26,]     0     0
## [27,]     0     1
## [28,]     0     1
## [29,]     0     1
## [30,]     1     1
## [31,]     1     1
## [32,]     1     1
## [33,]     0     1
## [34,]     1     0

# changing V
V(g)$size = betweenness(g, normalized = T, directed = FALSE) * 60 + 15  #after some trial and error
plot(g, mode = "undirected")

set.seed(2345)
l <- layout_with_mds(g)  #https://igraph.org/r/doc/layout_with_mds.html
plot(g, layout = l)

l  #let us take a look at the coordinates

##               [,1]         [,2]
##  [1,]  1.070931935 -0.172458113
##  [2,]  0.732844464  0.754023309
##  [3,]  0.100582299  0.397693607
##  [4,]  0.708246655  0.570205545
##  [5,]  1.816293170 -0.120778206
##  [6,]  1.881329566 -0.135518854
##  [7,]  1.881329566 -0.135518854
##  [8,]  0.812606714  0.472619437
##  [9,] -0.003769996  0.615513628
## [10,] -0.685680315  0.621065149
## [11,]  1.816293170 -0.120778206
## [12,]  1.621247830 -0.065820692
## [13,]  1.637845123  0.001789972
## [14,]  0.067317230  0.681421148
## [15,] -1.796316404  0.351417630
## [16,] -1.796316404  0.351417630
## [17,]  2.775260452 -0.124317652
## [18,]  1.616210024  0.182510197
## [19,] -1.796316404  0.351417630
## [20,]  0.048362858  0.566654982
## [21,] -1.796316404  0.351417630
## [22,]  1.616210024  0.182510197
## [23,] -1.796316404  0.351417630
## [24,] -1.891240567 -0.799574907
## [25,] -0.258345165 -2.006346563
## [26,] -0.360530857 -2.131642875
## [27,] -1.865177401  0.128596564
## [28,] -0.760226022 -0.529392331
## [29,] -0.710979936 -0.299960128
## [30,] -1.898426916 -0.149398746
## [31,] -0.568691923  0.804189411
## [32,] -0.048136037 -0.870967614
## [33,] -1.023681000 -0.035802363
## [34,] -1.146442924 -0.037605192

l[1, 1] <- 4
l[34, 1] <- -3.5
plot(g, layout = l)

## making network collaberations data, from JT's tutorial


fload <- function(filename) {
    load(filename)
    get(ls()[ls() != "filename"])
}

scholars <- fload("C:/Users/kalle/OneDrive/Documenten/REMA/Jaar 2/Social Networks/KS_labjournal/data/processed/scholars_20240924.rda")

#functie maken
fcolnet <- function(data = scholars, university = "RU", discipline = "sociology", waves = list(c(2015,
    2018), c(2019, 2023)), type = c("first")) {

    # step 1
    demographics <- do.call(rbind.data.frame, data$demographics)
    demographics <- demographics %>%
        mutate(Universiteit1.22 = replace(Universiteit1.22, is.na(Universiteit1.22), ""), Universiteit2.22 = replace(Universiteit2.22,
            is.na(Universiteit2.22), ""), Universiteit1.24 = replace(Universiteit1.24, is.na(Universiteit1.24),
            ""), Universiteit2.24 = replace(Universiteit2.24, is.na(Universiteit2.24), ""), discipline.22 = replace(discipline.22,
            is.na(discipline.22), ""), discipline.24 = replace(discipline.24, is.na(discipline.24), ""))

    sample <- which((demographics$Universiteit1.22 %in% university | demographics$Universiteit2.22 %in%
        university | demographics$Universiteit1.24 %in% university | demographics$Universiteit2.24 %in%
        university) & (demographics$discipline.22 %in% discipline | demographics$discipline.24 %in% discipline))

    demographics_soc <- demographics[sample, ]
    scholars_sel <- lapply(scholars, "[", sample)

    # step 2
    ids <- demographics_soc$au_id
    nwaves <- length(waves)
    nets <- array(0, dim = c(nwaves, length(ids), length(ids)), dimnames = list(wave = 1:nwaves, ids,
        ids))
    dimnames(nets)

    # step 3
    df_works <- tibble(works_id = unlist(lapply(scholars_sel$work, function(l) l$id)), works_author = unlist(lapply(scholars_sel$work,
        function(l) l$author), recursive = FALSE), works_year = unlist(lapply(scholars_sel$work, function(l) l$publication_year),
        recursive = FALSE))

    df_works <- df_works[!duplicated(df_works), ]

    # step 4
    if (type == "first") {
        for (j in 1:nwaves) {
            df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
                ]
            for (i in 1:nrow(df_works_w)) {
                ego <- df_works_w$works_author[i][[1]]$au_id[1]
                alters <- df_works_w$works_author[i][[1]]$au_id[-1]
                if (sum(ids %in% ego) > 0 & sum(ids %in% alters) > 0) {
                  nets[j, which(ids %in% ego), which(ids %in% alters)] <- 1
                }
            }
        }
    }

    if (type == "last") {
        for (j in 1:nwaves) {
            df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
                ]
            for (i in 1:nrow(df_works_w)) {
                ego <- rev(df_works_w$works_author[i][[1]]$au_id)[1]
                alters <- rev(df_works_w$works_author[i][[1]]$au_id)[-1]
                if (sum(ids %in% ego) > 0 & sum(ids %in% alters) > 0) {
                  nets[j, which(ids %in% ego), which(ids %in% alters)] <- 1
                }
            }
        }
    }

    if (type == "all") {
        for (j in 1:nwaves) {
            df_works_w <- df_works[df_works$works_year >= waves[[j]][1] & df_works$works_year <= waves[[j]][2],
                ]
            for (i in 1:nrow(df_works_w)) {
                egos <- df_works_w$works_author[i][[1]]$au_id
                if (sum(ids %in% egos) > 0) {
                  nets[j, which(ids %in% egos), which(ids %in% egos)] <- 1
                }
            }
        }
    }
    output <- list()
    output$data <- scholars_sel
    output$nets <- nets
    return(output)
}

1 Descriptive statistics for the network

#functie gebruiken

data <- fcolnet(data = scholars, 
                university = "RU", 
                discipline = "sociology", 
                waves = list(c(2015, 2018), c(2019, 2023)), 
                type = c("all"))

#first wave
net_w1 <- data$nets[1,,]
diag(net_w1) <- 0
g <- graph_from_adjacency_matrix(net_w1, mode = "undirected", diag = FALSE)

#density
density <- edge_density(g)
density

## [1] 0.04326531

#degree centrality
#ego level
degree(g)

## https://openalex.org/A5011326378 https://openalex.org/A5014129369 https://openalex.org/A5023395007 
##                                0                                3                                0 
## https://openalex.org/A5068642001 https://openalex.org/A5074062335 https://openalex.org/A5018242597 
##                                1                                0                                0 
## https://openalex.org/A5030977100 https://openalex.org/A5007673492 https://openalex.org/A5016107698 
##                                3                                7                                1 
## https://openalex.org/A5062608377 https://openalex.org/A5002388922 https://openalex.org/A5023494442 
##                                2                                2                                1 
## https://openalex.org/A5046746723 https://openalex.org/A5017382943 https://openalex.org/A5035350135 
##                                0                                0                               16 
## https://openalex.org/A5079372810 https://openalex.org/A5048988743 https://openalex.org/A5027461314 
##                                3                                5                               13 
## https://openalex.org/A5035502020 https://openalex.org/A5087380803 https://openalex.org/A5066699568 
##                                6                                4                                4 
## https://openalex.org/A5060015711 https://openalex.org/A5017017044 https://openalex.org/A5050987926 
##                                4                                8                                2 
## https://openalex.org/A5019030264 https://openalex.org/A5009655338 https://openalex.org/A5026217132 
##                                0                                0                                0 
## https://openalex.org/A5001803910 https://openalex.org/A5030092568 https://openalex.org/A5050683616 
##                                0                                0                                1 
## https://openalex.org/A5031371982 https://openalex.org/A5023362052 https://openalex.org/A5071959536 
##                                0                                0                                1 
## https://openalex.org/A5093927073 https://openalex.org/A5055096981 https://openalex.org/A5031002485 
##                                0                                0                                0 
## https://openalex.org/A5020765315 https://openalex.org/A5065130106 https://openalex.org/A5080235042 
##                                1                                0                                0 
## https://openalex.org/A5038009917 https://openalex.org/A5047687982 https://openalex.org/A5057934803 
##                                1                                2                                2 
## https://openalex.org/A5063338887 https://openalex.org/A5045572082 https://openalex.org/A5085493990 
##                                2                                2                                0 
## https://openalex.org/A5003892082 https://openalex.org/A5047911137 https://openalex.org/A5017637321 
##                                7                                0                                2 
## https://openalex.org/A5068000059 https://openalex.org/A5013258554 
##                                0                                0

#network level
mean(degree(g, mode = "all"))

## [1] 2.12

#transitivity
transitivity(g, type = "undirected")

## [1] 0.2743902

#plot
test_w1 <- igraph::graph_from_adjacency_matrix(
  data$nets[1,,], #for this example I take the first wave of data. (thus I select the array of networks and take the first matrix)
  mode = c("undirected"),
  weighted = NULL,
  diag = FALSE,
  add.colnames = NULL,
  add.rownames = NULL
)

## Warning: Same attribute for columns and rows, row names are ignored

plot(test_w1,
  vertex.label = NA,
  edge.width = 0.2,
  edge.arrow.size =0.2)

### second wave

#duidelijk meer density, centrality en minder isolates.

net_w2 <- data$nets[2,,]
diag(net_w2) <- 0
g2 <- graph_from_adjacency_matrix(net_w2, mode = "undirected", diag = FALSE)

#density
density2 <- edge_density(g2)
density2

## [1] 0.07428571

#degree centrality
#ego level
degree (g2)

## https://openalex.org/A5011326378 https://openalex.org/A5014129369 https://openalex.org/A5023395007 
##                                0                                2                                1 
## https://openalex.org/A5068642001 https://openalex.org/A5074062335 https://openalex.org/A5018242597 
##                                1                                1                                8 
## https://openalex.org/A5030977100 https://openalex.org/A5007673492 https://openalex.org/A5016107698 
##                                3                                9                                5 
## https://openalex.org/A5062608377 https://openalex.org/A5002388922 https://openalex.org/A5023494442 
##                                5                                2                                3 
## https://openalex.org/A5046746723 https://openalex.org/A5017382943 https://openalex.org/A5035350135 
##                                3                                0                               15 
## https://openalex.org/A5079372810 https://openalex.org/A5048988743 https://openalex.org/A5027461314 
##                                4                                7                               14 
## https://openalex.org/A5035502020 https://openalex.org/A5087380803 https://openalex.org/A5066699568 
##                               13                                8                                6 
## https://openalex.org/A5060015711 https://openalex.org/A5017017044 https://openalex.org/A5050987926 
##                                7                                3                                2 
## https://openalex.org/A5019030264 https://openalex.org/A5009655338 https://openalex.org/A5026217132 
##                                0                                2                                2 
## https://openalex.org/A5001803910 https://openalex.org/A5030092568 https://openalex.org/A5050683616 
##                                3                                2                                1 
## https://openalex.org/A5031371982 https://openalex.org/A5023362052 https://openalex.org/A5071959536 
##                                2                                2                                2 
## https://openalex.org/A5093927073 https://openalex.org/A5055096981 https://openalex.org/A5031002485 
##                                0                                4                                3 
## https://openalex.org/A5020765315 https://openalex.org/A5065130106 https://openalex.org/A5080235042 
##                                4                                3                                3 
## https://openalex.org/A5038009917 https://openalex.org/A5047687982 https://openalex.org/A5057934803 
##                                1                                1                                1 
## https://openalex.org/A5063338887 https://openalex.org/A5045572082 https://openalex.org/A5085493990 
##                                2                                4                                3 
## https://openalex.org/A5003892082 https://openalex.org/A5047911137 https://openalex.org/A5017637321 
##                               14                                0                                0 
## https://openalex.org/A5068000059 https://openalex.org/A5013258554 
##                                1                                0

#network level
mean(degree(g2, mode = "all"))

## [1] 3.64

#transitivity
transitivity(g2, type = "undirected")

## [1] 0.3226891

test_w2 <- igraph::graph_from_adjacency_matrix(
  data$nets[2,,],
  mode = c("undirected"),
  weighted = NULL,
  diag = FALSE,
  add.colnames = NULL,
  add.rownames = NULL
)

## Warning: Same attribute for columns and rows, row names are ignored

plot(test_w2,
  vertex.label = NA,
  edge.width = 0.2,
  edge.arrow.size =0.2)

2 Ego characteristics

# Scraping the # of citations of all papers published during per wave 


df_scholars <- do.call(rbind, scholars$demographics)

mail = "kalle.stoffers@ru.nl"


#empty list
citations_per_wave <- list()


for (author in df_scholars$au_id[1:675]) {

#fetch papers per authors
papers <- oa_fetch(
  entity = "works",
  author.id = author,
  mailto = mail)

# check if papers is NULL or has 0 rows
  if (is.null(papers) || nrow(papers) == 0) {
    # fill with 0 citations for both waves
    citations <- tibble(
      au_id = author,
      citations_w1 = 0,
      citations_w2 = 0
    )
  } else {
    # wave 1 citations
    citations_w1 <- papers |>
      filter(publication_year >= 2015 & publication_year <= 2018) |>
      summarise(citations_w1 = sum(cited_by_count, na.rm = TRUE))

    # wave 2 citations
    citations_w2 <- papers |>
      filter(publication_year >= 2019 & publication_year <= 2023) |>
      summarise(citations_w2 = sum(cited_by_count, na.rm = TRUE))

    # handle case where one of the summaries might return 0 rows
    if (nrow(citations_w1) == 0) citations_w1 <- tibble(citations_w1 = 0)
    if (nrow(citations_w2) == 0) citations_w2 <- tibble(citations_w2 = 0)

#join the two
citations <- merge(citations_w1, citations_w2)

citations <- citations|>
  mutate(au_id = author) |>
  relocate(au_id)
}
  
citations_per_wave[[author]] <- citations

}
  

#merge met originele dataset
citations_df <- bind_rows(citations_per_wave)

df <- left_join(df_scholars, citations_df, by = "au_id")


fsave <- function(x, file = NULL, location = "./data/processed/") {
    ifelse(!dir.exists("data"), dir.create("data"), FALSE)
    ifelse(!dir.exists("data/processed"), dir.create("data/processed"), FALSE)
    if (is.null(file))
        file = deparse(substitute(x))
    datename <- substr(gsub("[:-]", "", Sys.time()), 1, 8)
    totalname <- paste(location, file, "_", datename, ".rda", sep = "")
    save(x, file = totalname)  #need to fix if file is reloaded as input name, not as x. 
}

fsave(df)

#descriptives citations

df<- fload("~/REMA/Jaar 2/Social Networks/KS_labjournal/data/processed/df_20250929.rda")


summary(df$citations_w1)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     0.0    45.0   256.8   264.0 17808.0

summary (df$citations_w2)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0    12.0    64.0   174.5   181.0  5030.0

#remove outlier
df<- df |> filter(citations_w1 <= 6000)
summary (df$citations_w2)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0    12.0    64.0   167.3   180.8  4836.0

df_long <- df|>
  pivot_longer(cols = c(citations_w1, citations_w2),
               names_to = "wave",
               names_prefix = "citations_w",
               values_to = "citations") |>
  mutate(wave = as.factor(wave))

ggplot(df_long, aes(x = citations, color = wave)) + geom_density()