In-class Exercise 5

0.1 Installing and Loading the Required Libraries

pacman::p_load(tidyverse, jsonlite, SmartEDA, tidygraph, ggraph)

0.2 Importing Data

kg <- fromJSON("data/MC1_graph.json")

0.2.1 Inspect structure

str(kg,max.level = 1)

List of 5
 $ directed  : logi TRUE
 $ multigraph: logi TRUE
 $ graph     :List of 2
 $ nodes     :'data.frame': 17412 obs. of  10 variables:
 $ links     :'data.frame': 37857 obs. of  4 variables:

0.2.2 Extract and inspect

nodes_tbl <- as_tibble(kg$nodes)
edges_tbl <- as_tibble(kg$links)

0.3 Initial EDA

ggplot(data = edges_tbl,
       aes(y = `Edge Type`)) +
  geom_bar()

ggplot(data = nodes_tbl,
       aes(y = `Node Type`)) +
  geom_bar()

0.4 Creating Knowledge Graph

0.4.1 Step 1: Mapping from node id to row index

id_map <- tibble(id = nodes_tbl$id,
                 index = seq_len(
                   nrow(nodes_tbl)))

This ensures each id from your node list is mapped the correct row number.

0.4.2 Step 2: Map source and target IDs to row indices

edges_tbl <- edges_tbl %>%
  left_join(id_map, by = c("source" = "id")) %>%
  rename(from = index) %>%
  left_join(id_map, by = c("target" = "id")) %>%
  rename(to = index)

0.4.3 Step 3: Filter out any unmatched (invalid) edges

edges_tbl <- edges_tbl %>%
  filter(!is.na(from), !is.na(to))

0.4.4 Step 4: Creating the graph

graph <- tbl_graph(nodes = nodes_tbl,
                   edges = edges_tbl,
                   directed = kg$directed)

0.5 Visualising the knowledge graph

set.seed(1234)

0.6 Visualising the whole graph

ggraph(graph, layout = "fr") +
  geom_edge_link(alpha = 0.3,
                 colour = "gray") +
  geom_node_point(aes(colour = "Node Type"),
                  size = 4) +
  geom_node_text(aes(label = name),
                 repel = TRUE,
                 size = 2.5) +
  theme_void()

0.7 Visualising the sub-graph

0.7.1 Step 1: Filter edges to only “MemberOf”

graph_memberof <- graph %>%
  activate(edges) %>%
  filter(`Edge Type` == "MemberOf")

0.7.2 Step 2: Extract only connected nodes (i.e., used in these edges)

used_node_indices <- graph_memberof %>%
  activate(edges) %>%
  as_tibble() %>%
  select(from, to) %>%
  unlist() %>%
  unique()

0.7.3 Step 3: Keep only those nodes

graph_memberof <- graph_memberof %>%
  activate(nodes) %>%
  mutate(row_id = row_number()) %>%
  filter(row_id %in% used_node_indices) %>%
  select(-row_id) #optional cleanup

0.7.4 Plot the sub-graph

ggraph(graph_memberof,
       layout = "fr") +
  geom_edge_link(alpha = 0.5,
                 colour ="gray") +
  geom_node_point(aes(colour = `Node Type`),
                      size = 1) +
  geom_node_text(aes(label = name),
                 repel = TRUE,
                 size = 2.5) +
  theme_void()

Warning: ggrepel: 789 unlabeled data points (too many overlaps). Consider
increasing max.overlaps