R code for this note: http://f.briatte.org/r/turning-keywords-into-a-co-occurrence-network

0_keywords.txt

name	keywords
Felicitas	Development, Corruption, Housing, Transportation, Religion
Lucile	Inequality, Migration, Race and Gender, Community/Neighborhood
Corentin	Inequality, Religion, Migration, Poverty, Gender
Charlotte	Energy, Environment, Smart Cities, Conflict, Gender
Alice	Environment, Gender, Migration, Urban Studies
Miranda	Politics, Gender, Human Rights
Margaux	Inequality, Urban Studies, Migration, Human Rights
Marina	Critical Theory, Social Inequality, Race and Gender
Cosima	Race and Gender and Sexuality, Spatial Inequality, Migration 
Isabelle	Slums, Poverty, Housing, Inequality
Elena	Race and Gender, Urban Inequality, Immigrant Integration, Collective Action
Gabriel	Environment, Collective Action, Urban Studies
Alexander	Transportation, Environmental Justice, Community Empowerment
Akhil	Tourism, Transportation, Politics
Gabriella	Multicultural Cohabitation, Environment, Radical Politics and Space Appropriation
Mohamed	Transport, Inequality, Poverty, Spatial Inequality
Lucien	Inequality, Poverty, Religious Segregation, Migration

1_packages.r

library(dplyr)
library(ggnetwork)
library(ggplot2)
library(readr)
library(stringr)
library(tnet)
library(network) # keep after tnet

2_co-occurring_keyword_frequencies.r

read_tsv("keywords.txt")$keywords %>%
  str_split(", | and ") %>%
  unlist %>%
  table %>%
  data.frame %>%
  arrange(-Freq) %>%
  filter(Freq > 1)

3_weighted_edge_list.r

e <- read_tsv("keywords.txt")$keywords %>%
  str_split(", | and ") %>%
  lapply(function(x) {
    expand.grid(x, x, w = 1 / length(x), stringsAsFactors = FALSE)
  }) %>%
  bind_rows

e <- apply(e[, -3], 1, str_sort) %>%
  t %>%
  data.frame(stringsAsFactors = FALSE) %>%
  mutate(w = e$w)

4_newman_fowler_weights.r

e <- group_by(e, X1, X2) %>%
  summarise(w = sum(w)) %>%
  filter(X1 != X2)

5_network_object.r

# undirected network
n <- network(e[, -3], directed = FALSE)

stopifnot(nrow(e) == network.edgecount(n))
set.edge.attribute(n, "weight", e$w)

# weighted degree at alpha = 1
t <- as.edgelist(n, attrname = "weight") %>%
  symmetrise_w %>%
  as.tnet %>%
  degree_w

stopifnot(nrow(t) == network.size(n))
set.vertex.attribute(n, "degree_w", t[, "output" ])

6_network_labels.r

# show only keywords at or above median weighted degree
l <- n %v% "degree_w"
l <- ifelse(l >= median(l), network.vertex.names(n), NA)

stopifnot(length(l) == network.size(n))
set.vertex.attribute(n, "label", l)

7_network_plot.r

ggplot(n, aes(x, y, xend = xend, yend = yend)) +
  geom_edges(aes(color = weight)) +
  geom_nodes(color = "grey50") +
  geom_nodelabel(aes(size = degree_w, label = label),
                 color = "grey20", label.size = NA) +
  scale_size_continuous(range = c(2, 8)) +
  scale_color_gradient2(low = "grey25", midpoint = 0.75, high = "black") +
  guides(size = FALSE, color = FALSE) + 
  theme_blank()

"n" is not a graph obejct, see line set.edge.attribute(n, "weight", e$w)

I've used igraph for the creation of the network.

The code uses {network}, not {igraph}.