Take-home_Ex03

Author

Zou Jiaxun

Published

June 7, 2024

Vast Challenge 2024 Mini-Challenge 3

In this challenge, I will attempt to do the following:

    1. Use visual analytics to identify the relationship of entities
    1. Based on the visualization on Network, infer a network ownership or influence

1 Getting Start

1.1 Package Preparation

Click to view code 
pacman::p_load(tidyverse, readtext, skimr, knitr,
               quanteda, tidytext, jsonlite, dplyr,
               tidyr, tidygraph, ggraph, igraph, lubridate,
               visNetwork, ggplot2, gganimate, gridExtra)

2 Data Wangling

2.1 Data Import

Click to view code 
mc3_data <- fromJSON("data/MC3/mc3 1.json")
class(mc3_data)
[1] "list"

2.2 Data Check

Click to view code 
mc3_edges <- as_tibble(mc3_data$link)
glimpse(mc3_edges)
Rows: 75,817
Columns: 11
$ start_date          <chr> "2016-10-29T00:00:00", "2035-06-03T00:00:00", "202…
$ type                <chr> "Event.Owns.Shareholdership", "Event.Owns.Sharehol…
$ `_last_edited_by`   <chr> "Pelagia Alethea Mordoch", "Niklaus Oberon", "Pela…
$ `_last_edited_date` <chr> "2035-01-01T00:00:00", "2035-07-15T00:00:00", "203…
$ `_date_added`       <chr> "2035-01-01T00:00:00", "2035-07-15T00:00:00", "203…
$ `_raw_source`       <chr> "Existing Corporate Structure Data", "Oceanus Corp…
$ `_algorithm`        <chr> "Automatic Import", "Manual Entry", "Automatic Imp…
$ source              <chr> "Avery Inc", "Berger-Hayes", "Bowers Group", "Bowm…
$ target              <chr> "Allen, Nichols and Thompson", "Jensen, Morris and…
$ key                 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ end_date            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
Click to view code 
mc3_nodes <- as_tibble(mc3_data$nodes)
glimpse(mc3_nodes)
Rows: 60,520
Columns: 15
$ type                <chr> "Entity.Organization.Company", "Entity.Organizatio…
$ country             <chr> "Uziland", "Mawalara", "Uzifrica", "Islavaragon", …
$ ProductServices     <chr> "Unknown", "Furniture and home accessories", "Food…
$ PointOfContact      <chr> "Rebecca Lewis", "Michael Lopez", "Steven Robertso…
$ HeadOfOrg           <chr> "Émilie-Susan Benoit", "Honoré Lemoine", "Jules La…
$ founding_date       <chr> "1954-04-24T00:00:00", "2009-06-12T00:00:00", "202…
$ revenue             <dbl> 5994.73, 71766.67, 0.00, 0.00, 4746.67, 46566.67, …
$ TradeDescription    <chr> "Unknown", "Abbott-Gomez is a leading manufacturer…
$ `_last_edited_by`   <chr> "Pelagia Alethea Mordoch", "Pelagia Alethea Mordoc…
$ `_last_edited_date` <chr> "2035-01-01T00:00:00", "2035-01-01T00:00:00", "203…
$ `_date_added`       <chr> "2035-01-01T00:00:00", "2035-01-01T00:00:00", "203…
$ `_raw_source`       <chr> "Existing Corporate Structure Data", "Existing Cor…
$ `_algorithm`        <chr> "Automatic Import", "Automatic Import", "Automatic…
$ id                  <chr> "Abbott, Mcbride and Edwards", "Abbott-Gomez", "Ab…
$ dob                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…

2.3 Data Filter

Click to view code 
# Assume the end_data which is NA be filled by 2035-12-12
mc3_edges_add <- mc3_edges %>%
  mutate(
    start_date = as.Date(start_date, format = "%Y-%m-%d"),
    end_date = as.Date(ifelse(is.na(end_date), "2035-12-12", as.character(end_date)), format = "%Y-%m-%d")
  ) %>%
  filter(!is.na(start_date))

# Display the modified dataset
glimpse(mc3_edges_add)
Rows: 75,727
Columns: 11
$ start_date          <date> 2016-10-29, 2035-06-03, 2028-11-20, 2024-09-04, 2…
$ type                <chr> "Event.Owns.Shareholdership", "Event.Owns.Sharehol…
$ `_last_edited_by`   <chr> "Pelagia Alethea Mordoch", "Niklaus Oberon", "Pela…
$ `_last_edited_date` <chr> "2035-01-01T00:00:00", "2035-07-15T00:00:00", "203…
$ `_date_added`       <chr> "2035-01-01T00:00:00", "2035-07-15T00:00:00", "203…
$ `_raw_source`       <chr> "Existing Corporate Structure Data", "Oceanus Corp…
$ `_algorithm`        <chr> "Automatic Import", "Manual Entry", "Automatic Imp…
$ source              <chr> "Avery Inc", "Berger-Hayes", "Bowers Group", "Bowm…
$ target              <chr> "Allen, Nichols and Thompson", "Jensen, Morris and…
$ key                 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ end_date            <date> 2035-12-12, 2035-12-12, 2035-12-12, 2035-12-12, 2…
Click to view code 
mc3_edges_filt <- mc3_edges_add %>% select(type, source, target, start_date, end_date)
glimpse(mc3_edges_filt)
Rows: 75,727
Columns: 5
$ type       <chr> "Event.Owns.Shareholdership", "Event.Owns.Shareholdership",…
$ source     <chr> "Avery Inc", "Berger-Hayes", "Bowers Group", "Bowman-Howe",…
$ target     <chr> "Allen, Nichols and Thompson", "Jensen, Morris and Downs", …
$ start_date <date> 2016-10-29, 2035-06-03, 2028-11-20, 2024-09-04, 2034-11-12…
$ end_date   <date> 2035-12-12, 2035-12-12, 2035-12-12, 2035-12-12, 2035-12-12…
Click to view code 
mc3_nodes_filt <- mc3_nodes %>% select(type, id, country)
glimpse(mc3_nodes_filt)
Rows: 60,520
Columns: 3
$ type    <chr> "Entity.Organization.Company", "Entity.Organization.Company", …
$ id      <chr> "Abbott, Mcbride and Edwards", "Abbott-Gomez", "Abbott-Harriso…
$ country <chr> "Uziland", "Mawalara", "Uzifrica", "Islavaragon", "Oceanus", "…

2.4 Edges & Nodes Explore

Click to view code 
skim(mc3_edges_filt)
Data summary
Name mc3_edges_filt
Number of rows 75727
Number of columns 5
_______________________
Column type frequency:
character 3
Date 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
type 0 1 14 31 0 4 0
source 0 1 6 42 0 51995 0
target 0 1 6 48 0 8872 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
start_date 0 1 1952-05-31 2035-12-29 2023-12-12 11468
end_date 0 1 2035-01-01 2035-12-29 2035-12-12 73
Click to view code 
p1 <- ggplot(data = mc3_edges_filt, aes(x = type, fill = type)) +
  geom_bar() + theme(axis.text.x = element_text(angle = 60, hjust = 1))
p1

Click to view code 
skim(mc3_nodes_filt)
Data summary
Name mc3_nodes_filt
Number of rows 60520
Number of columns 3
_______________________
Column type frequency:
character 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
type 0 1 13 36 0 8 0
id 0 1 6 48 0 60520 0
country 0 1 4 15 0 86 0
Click to view code 
p2 <- ggplot(data = mc3_nodes_filt, aes(x = type, fill = type)) +
  geom_bar() + theme(axis.text.x = element_text(angle = 60, hjust = 1))
p2

Tip

According to the VAST2024 - MC3 Data Description, the person node include Entity.Person and Entity.Person.CEO, the other were included in the Company.

3 MC3 Task

Tip

Based on this plot, the edge date will be used, and the company name to define company; Meanwhile, the type in node will be used, source and target will be choosed to decide the “from” and “to”.

3.1 Relationship

The code chunk below will be used to extract the nodes data.frame of mc3_data and save it as a tibble data.frame called mc3_nodes.

Click to view code 
# Clean and select columns from mc3_data$nodes
mc3_nodes_clean <- as_tibble(mc3_nodes_filt) %>%
  mutate(type = type,
         id = id) %>%
  select(id, type) %>%
  separate(type, into = c("prefix", "supertype", "subtype"), sep = "\\.", extra = "merge", fill = "right") %>%
  select(id, supertype, subtype)

kable(head(mc3_nodes_clean))
id supertype subtype
Abbott, Mcbride and Edwards Organization Company
Abbott-Gomez Organization Company
Abbott-Harrison Organization Company
Abbott-Ibarra Organization Company
Abbott-Sullivan Organization Company
Acevedo and Sons Organization Company

The code chunk below will be used to extract the links data.frame of mc3_data and save it as a tibble data.frame called mc3_edges.

Click to view code 
mc3_edges_clean <- as_tibble(mc3_edges_filt) %>% 
  mutate(
    start_date = as_datetime(start_date),
    end_date = as_datetime(end_date)
    ) %>%
  group_by(source, target, type, start_date, end_date) %>%
    summarise(weights = n(), .groups = 'drop') %>%
  filter(source != target) %>%
  ungroup()
kable(head(mc3_edges_clean))
source target type start_date end_date weights
4. SeaCargo Ges.m.b.H. Dry CreekRybachit Marine A/S Event.Owns.Shareholdership 2034-12-31 2035-12-12 1
4. SeaCargo Ges.m.b.H. KambalaSea Freight Inc Event.Owns.Shareholdership 2033-04-12 2035-12-12 1
9. RiverLine CJSC SumacAmerica Transport GmbH & Co. KG Event.Owns.Shareholdership 2028-12-02 2035-12-12 1
Aaron Acosta Manning-Pratt Event.Owns.Shareholdership 2008-09-14 2035-12-12 1
Aaron Acosta Manning-Pratt Event.WorksFor 2008-07-30 2035-12-12 1
Aaron Allen Hicks-Calderon Event.Owns.BeneficialOwnership 2025-03-06 2035-12-12 1

Let’s focuse on the SouthSeafood Express Corp

Click to view code 
# Define the target node
target_node <- "SouthSeafood Express Corp"

# Filter edges related to the target node
related_edges <- mc3_edges_clean %>%
  filter(source == target_node | target == target_node) %>%
  rename(from = source, to = target)

# Extract the related nodes
related_nodes <- mc3_nodes_clean %>%
  filter(id %in% c(related_edges$from, related_edges$to))


# Create the visNetwork plot
visNetwork(related_nodes, related_edges) %>%
  visPhysics(solver = "forceAtlas2Based",
             forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visGroups(groupname = "Organization", color = "pink") %>%
  visGroups(groupname = "Person", color = "lightgreen") %>%
  visLegend() %>%
  visEdges(arrows = "to") %>%
  visOptions(
    highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
    nodesIdSelection = TRUE,
    selectedBy = "subtype",
    collapse = TRUE
  ) %>%
  visInteraction(dragNodes = TRUE, dragView = TRUE, zoomView = TRUE) %>%
  visEvents(selectNode = "function(properties) {
    var selectedNodeId = properties.nodes[0];
    this.body.data.nodes.update({id: selectedNodeId, color: {background: 'red', border: 'black'}});
  }")
Click to view code 
# Define the target node
target_node <- "SouthSeafood Express Corp"

# Filter edges related to the target node
initial_edges <- mc3_edges_clean %>%
  filter(source == target_node | target == target_node) %>%
  rename(from = source, to = target)

connected_nodes <- mc3_nodes_clean %>%
  filter(id %in% c(initial_edges$from, initial_edges$to))

expanded_edges <- mc3_edges_clean %>%
  filter(source %in% connected_nodes$id | target %in% connected_nodes$id) %>%
  rename(from = source, to = target)

# Extract source and target nodes from expanded edges
expanded_nodes_source <- expanded_edges %>%
  distinct(from) %>%
  rename(id = from)

expanded_nodes_target <- expanded_edges %>%
  distinct(to) %>%
  rename(id = to)

# Combine and deduplicate nodes
expanded_nodes_combined <- bind_rows(expanded_nodes_source, expanded_nodes_target) %>%
  distinct(id) %>%
  left_join(mc3_nodes_clean, by = "id")

# Assign group based on supertype
expanded_nodes_combined$group <- ifelse(expanded_nodes_combined$supertype == "Organization", "Organization", "Person")


# Create the visNetwork plot
visNetwork(expanded_nodes_combined, expanded_edges) %>%
  visPhysics(solver = "forceAtlas2Based",
             forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visGroups(groupname = "Organization", color = "pink") %>%
  visGroups(groupname = "Person", color = "lightgreen") %>%
  visLegend() %>%
  visEdges(arrows = "to") %>%
  visOptions(
    highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
    nodesIdSelection = TRUE,
    selectedBy = "subtype",
    collapse = TRUE
  ) %>%
  visInteraction(dragNodes = TRUE, dragView = TRUE, zoomView = TRUE) %>%
  visEvents(selectNode = "function(properties) {
    var selectedNodeId = properties.nodes[0];
    this.body.data.nodes.update({id: selectedNodeId, color: {background: 'red', border: 'black'}});
  }")
Click to view code 
# Identify nodes directly connected to the target node
connected_nodes <- mc3_nodes_clean %>%
  filter(id %in% c(initial_edges$from, initial_edges$to))

expanded_edges <- initial_edges
expanded_nodes <- connected_nodes

repeat {
  # Identify all nodes connected to the current set of nodes
  new_edges <- mc3_edges_clean %>%
    filter(source %in% expanded_nodes$id | target %in% expanded_nodes$id) %>%
    rename(from = source, to = target)
  
  # Identify new nodes from the newly found edges
  new_nodes <- mc3_nodes_clean %>%
    filter(id %in% c(new_edges$from, new_edges$to)) %>%
    filter(!id %in% expanded_nodes$id)
  
  # Add new edges and nodes to the expanded set
  expanded_edges <- bind_rows(expanded_edges, new_edges) %>%
    distinct()
  expanded_nodes <- bind_rows(expanded_nodes, new_nodes) %>%
    distinct()
  
  # Break the loop if there are no new nodes to add or all new nodes are of type "Person"
  if (nrow(new_nodes) == 0 || all(new_nodes$supertype == "Person")) {
    break
  }
}
Click to view code 
# Extract source and target nodes from expanded edges
expanded_nodes_source <- expanded_edges %>%
  distinct(from) %>%
  rename(id = from)

expanded_nodes_target <- expanded_edges %>%
  distinct(to) %>%
  rename(id = to)

# Combine and deduplicate nodes
expanded_nodes_combined <- bind_rows(expanded_nodes_source, expanded_nodes_target) %>%
  distinct(id) %>%
  left_join(mc3_nodes_clean, by = "id")

# Assign group based on supertype
expanded_nodes_combined$group <- ifelse(expanded_nodes_combined$supertype == "Organization", "Organization", "Person")
Click to view code 
# Create the visNetwork plot
visNetwork(expanded_nodes_combined, expanded_edges) %>%
  visPhysics(solver = "forceAtlas2Based",
             forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visGroups(groupname = "Organization", color = "pink") %>%
  visGroups(groupname = "Person", color = "lightgreen") %>%
  visLegend() %>%
  visEdges(arrows = "to") %>%
  visOptions(
    highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
    nodesIdSelection = TRUE,
    selectedBy = "subtype",
    collapse = TRUE
  ) %>%
  visInteraction(dragNodes = TRUE, dragView = TRUE, zoomView = TRUE) %>%
  visEvents(selectNode = "function(properties) {
    var selectedNodeId = properties.nodes[0];
    this.body.data.nodes.update({id: selectedNodeId, color: {background: 'red', border: 'black'}});
  }")

3.2 Visualising network graph

First, create a graph object using tbl_graph() function. Then calculate betweenness and closeness centrality scores.

Click to view code 
mc3_graph <- tbl_graph(nodes = mc3_nodes_clean,
                       edges = mc3_edges_clean,
                       directed = FALSE) %>%
  mutate(betweenness_centrality = centrality_betweenness(),
         closeness_centrality = centrality_closeness())

We will filter nodes with high betweeness centrality scores (>8,000,000) and visualise them to see the relationships that they have.

Click to view code 
set.seed(1234)
mc3_graph %>%
  filter(betweenness_centrality > 8000000) %>%
ggraph(layout = "fr") +
  geom_edge_link(aes(alpha=0.5)) +
  geom_node_point(aes(
    size = betweenness_centrality,
    color = supertype,
    alpha = 0.3)) +
  geom_node_label(aes(label = id),repel=TRUE, size=2.5, alpha = 0.8) +
  scale_size_continuous(range=c(1,10)) +
  theme_graph() +
  labs(title = 'Initial network visualisation',
       subtitle = 'Entities with betweenness scores > 8,000,000')

Below is a dataframe showing us the top 10 entities with the highest betweenness scores.

Click to view code 
mc3_graph %>%  activate(nodes) %>%  as_tibble() %>% arrange(desc(betweenness_centrality)) %>% slice(1:10) %>% kable()
id supertype subtype betweenness_centrality closeness_centrality
Sharon Moon Person NA 31170638 3.6e-06
Steven Robertson Person NA 28158072 3.2e-06
Johnson, Morales and Castro Organization Company 26218503 3.7e-06
Hart Ltd Organization FinancialCompany 26129255 3.2e-06
Abbott-Harrison Organization Company 24206713 3.0e-06
Phelps, Montoya and Barnett Organization Company 21073981 3.0e-06
Morrison-Zamora Organization Company 20813895 3.7e-06
Blankenship-Strickland Organization Company 20157207 3.4e-06
Holloway-Salas Organization Company 19957178 3.7e-06
Michael Howard DDS Person NA 19922437 3.5e-06

The top 10 betweenness entities above include 3 persons and 7 companies. In the next section, we will filter entities into only organization entities. We may revisit the person entities later when we have specific targets/companies to investigate.

3.3 Check the Illegal event timeline

  1. before 2033-10-29
  2. between 2033-10-29 and 2035-05-25
  3. after 2035-05-25
Click to view code 
# Time bins
bin1 <- mc3_edges_clean %>%
  filter(as.Date(start_date) < as.Date("2033-10-29"))

bin2 <- mc3_edges_clean %>%
  filter(as.Date(start_date) >= as.Date("2033-10-29") & as.Date(start_date) <= as.Date("2035-05-25"))

bin3 <- mc3_edges_clean %>%
  filter(as.Date(start_date) > as.Date("2035-05-25"))

# Filter edges to include any that contain "SouthSeafood"
southseafood_edges_bin1 <- bin1 %>%
  filter(str_detect(source, "SouthSeafood") | str_detect(target, "SouthSeafood"))

southseafood_edges_bin2 <- bin2 %>%
  filter(str_detect(source, "SouthSeafood") | str_detect(target, "SouthSeafood"))

southseafood_edges_bin3 <- bin3 %>%
  filter(str_detect(source, "SouthSeafood") | str_detect(target, "SouthSeafood"))

glimpse(southseafood_edges_bin1)
Rows: 0
Columns: 6
$ source     <chr> 
$ target     <chr> 
$ type       <chr> 
$ start_date <dttm> 
$ end_date   <dttm> 
$ weights    <int>
Click to view code 
glimpse(southseafood_edges_bin2)
Rows: 2
Columns: 6
$ source     <chr> "AguaLeska Transit N.V.", "Tainamarine Fishing Co"
$ target     <chr> "SouthSeafood Express Corp", "SouthSeafood Express Corp"
$ type       <chr> "Event.Owns.Shareholdership", "Event.Owns.Shareholdership"
$ start_date <dttm> 2033-10-29, 2035-05-25
$ end_date   <dttm> 2035-05-25, 2035-12-12
$ weights    <int> 1, 1
Click to view code 
glimpse(southseafood_edges_bin3)
Rows: 0
Columns: 6
$ source     <chr> 
$ target     <chr> 
$ type       <chr> 
$ start_date <dttm> 
$ end_date   <dttm> 
$ weights    <int>

We notice that, there are only 2 companies related to the SouthSeafood Express Corp, they might be shell company.

3.4 Exploring the VisNetwork for company influence based on timeline

3.4.1 Filter nodes to include only Organizations

Click to view code 
# Filter nodes to include only Organizations or Persons
mc3_nodes_select <- mc3_nodes_clean %>%
  filter(grepl("Organization|Person", supertype, ignore.case = TRUE))

3.4.3 Extract edges with high connection

Click to view code 
mc3_edges_select_high <- mc3_edges_select %>%
  group_by(from) %>%
  mutate(count = n()) %>%
  filter(count >= 92) %>%
  ungroup()

3.4.4 Distinct source and target

Click to view code 
mc3_nodes_source <- mc3_edges_select_high %>%
  distinct(from) %>%
  rename(id = from)

mc3_nodes_target <- mc3_edges_select_high %>%
  distinct(to) %>%
  rename(id = to)

3.4.5 Build visNetwork

Click to view code 
# Combine and deduplicate nodes
mc3_nodes_combined <- bind_rows(mc3_nodes_source, mc3_nodes_target) %>%
  distinct(id) %>%
  left_join(mc3_nodes_clean, by = "id")

# Assign group based on supertype
mc3_nodes_combined$group <- ifelse(mc3_nodes_combined$supertype == "Organization", "Organization", "Person")

# Visualize with visNetwork
visNetwork(mc3_nodes_combined, mc3_edges_select_high) %>%
  visPhysics(solver = "forceAtlas2Based",
             forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visGroups(groupname = "Organization", color = "yellow") %>%
  visGroups(groupname = "Person", color = "grey") %>%
  visLegend() %>%
  visEdges(arrows = "to") %>%
  visOptions(
    highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
    nodesIdSelection = TRUE,
    selectedBy = "subtype",
    collapse = TRUE
  ) %>%
  visInteraction(dragNodes = TRUE, dragView = TRUE, zoomView = TRUE) %>%
  visEvents(selectNode = "function(properties) {
    var selectedNodeId = properties.nodes[0];
    this.body.data.nodes.update({id: selectedNodeId, color: {background: 'red', border: 'black'}});
  }")

3.4.6 Compare timeline

Since the SouthSeafood Express Corp connect first event from AguaLeska Transit N.V. in 2033-10-29, and turn to connect Tainamarine Fishing Co in 2035-05-25

Click to view code 
# Filter edges based on date criteria
mc3_edges_filtered_dates <- mc3_edges_clean %>%
  filter(start_date < as_datetime("2033-10-29") & end_date > as_datetime("2033-10-29"))

# Visualize with visNetwork
visNetwork(mc3_nodes_combined, mc3_edges_select_high) %>%
  visPhysics(solver = "forceAtlas2Based",
             forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visGroups(groupname = "Organization", color = "lightblue") %>%
  visGroups(groupname = "Person", color = "lightgreen") %>%
  visLegend() %>%
  visEdges(arrows = "to") %>%
  visOptions(
    highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
    nodesIdSelection = TRUE,
    selectedBy = "subtype",
    collapse = TRUE
  ) %>%
  visInteraction(dragNodes = TRUE, dragView = TRUE, zoomView = TRUE) %>%
  visEvents(selectNode = "function(properties) {
    var selectedNodeId = properties.nodes[0];
    this.body.data.nodes.update({id: selectedNodeId, color: {background: 'red', border: 'black'}});
  }")
Click to view code 
mc3_edges_filtered_dates <- mc3_edges_clean %>%
  filter(end_date > as_datetime("2035-05-25"))


# Visualize with visNetwork
visNetwork(mc3_nodes_combined, mc3_edges_select_high) %>%
  visPhysics(solver = "forceAtlas2Based",
             forceAtlas2Based = list(gravitationalConstant = -100)) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visGroups(groupname = "Organization", color = "lightblue") %>%
  visGroups(groupname = "Person", color = "lightgreen") %>%
  visLegend() %>%
  visEdges(arrows = "to") %>%
  visOptions(
    highlightNearest = list(enabled = TRUE, degree = 2, hover = TRUE),
    nodesIdSelection = TRUE,
    selectedBy = "subtype",
    collapse = TRUE
  ) %>%
  visInteraction(dragNodes = TRUE, dragView = TRUE, zoomView = TRUE) %>%
  visEvents(selectNode = "function(properties) {
    var selectedNodeId = properties.nodes[0];
    this.body.data.nodes.update({id: selectedNodeId, color: {background: 'red', border: 'black'}});
  }")

We can see from the visNetwork, some person control more than 30 companies at the same time, there will be an obvious control of illegal financial activities.

5.Conclusion

Exploring the networks between the various type of nodes or players in the space has been useful to visualising the relationships between the different parties. It has yielded interesting insights on how certain companies may influence the around companies.

For future work, the additional column of timeline and financial situation can be used to provide an additional layer of to the overall visualisation of networks and information in this project.