Preparation for VAST 2025 Mini Challenge 1

1 Loading Packages

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

2 Importing Knowledge Graph Data

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

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:

2.2 Extract and Inspect

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

2.3 Initial EDA

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

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

3 Creating Knowledge Graph

3.1 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 to the correct row number

3.2 Step 2 : Map Source and Target IDs to row indicises

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)

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

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

3.4 Step 4 : Creating the Graph

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

3.5 Visualising the knowledge graph

set.seed(1234)

3.6 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()

4 Visualising The Sub Graph

4.1 Step 1 : Filter Edges to only “MemberOf”

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

4.2 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()

4.3 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)

4.4 Plot the sub-graph

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