In-class_Ex03

In the code chunk below, p_load() of pacman package is used to load the R packages into R environment.

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

Importing knowledge Graph Data

kg <- fromJSON("MC1_release/MC1_graph.json")

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:

Extract and inspect

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

Initial EDA

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

Creating Knowledge Graph

This is

Step 1: Mapping from node id to row index

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

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

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)

Step 3: Filter out any unmatched

(invalid) edges

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

Step 4: Creating the graph

Lastly, tbl_graph() is used to create tidygraph’s graph pbject by using the code chunk below.

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

Visualizing the knowledge graph

set.seed(1234)

Visualising the whole graph

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

Visualising the sub-graph

In this section, we are interested to create a sub-graph based on MemberOf value in Edge_Type column of the edges column of the edge data frame.

Step 1: Filter edges to only “MemberOf”

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

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()

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

Plot the sub-graph

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