Fishy Similarities

VAST Challenge 2023: Mini-Challenge 3

1 Project Brief

FishEye International, a non-profit focused on countering illegal, unreported, and unregulated (IUU) fishing, has been given access to an international finance corporation’s database on fishing related companies. In the past, FishEye has determined that companies with anomalous structures are far more likely to be involved in IUU (or other fishy business). FishEye has transformed the database into a knowledge graph, including information about companies, owners, workers, and financial status. FishEye is aiming to use this graph to identify anomalies that could indicate if a company is involved in IUU.

Project Objective:

This study aims to use visual analytics to

• measure similarity of business groups
• provide evidence for and against the case that anomalous companies are involved in illegal fishing

This will be investigated through both visual and quantitative means: visualising the networks from various industries present in the network graph could help identify if anomalous network structures uncovered in Fishy Business are more prominent or present in certain industries. Network Similarity measures will also be used to quantify these similarities from the graph objects.

2 Data Preparation

2.1 R Packages

The following packages are used for this study:

• tidyverse, a collection of packages for data analysis (particularly dplyr for data manipulation)

• ggraph, igraph and visNetwork for generating network graph visuals

• NetworkSim for calculation of Similarity Measures

• kableExtra for styling tables from dataframes

pacman::p_load(tidyverse, ggrepel, DT, kableExtra, ggplot2, scales, ggthemes, visNetwork, ggraph, igraph, gganimate, tidygraph, ggpubr, htmlwidgets, NetworkSim)

2.2 Loading the Data

Cleaned data with groupings are taken from the previous analysis, Fishy Businesses:

mc3_links_new <- read_csv("data/mc3_links_new.csv")
mc3_nodes_all <- read_csv("data/mc3_shinynodes.csv")

3 Visualising Network Structures By Industry

The following groups were derived from topic modeling of products and services offered by companies in the data:

Topic.	Industry	Description
1	Industrial	Manages equipment, machinery and other industrial materials
2	Food	Vegetables, meat, fruits and other groceries
3	Seafood-processing	Packaging, canning, manufacturing of marine or seafood products
4	Consumer-goods	Non-fishing related accessories, furniture, apparel
5	Transport-logistics	Companies specialising in logistics, freight, cargo services
6	Fishing	Companies directly related to fishing of salmon, tuna, etc

As there were some companies that had a high probability of being classified under more than a single industry, these were grouped together as a separate category, multi-industry companies.

I. Filter records belonging to companies in different industries

Create separate dataframes for nodes in each Industry to use as filters for links data.

# Overall Company nodes
nodes_industry <- mc3_nodes_all %>%
  filter(grepl("company", group, ignore.case = TRUE) & group != "Company Contact")

# industrial companies
nodes_industrial <- nodes_industry %>%
  filter(group == "Industrial Company")

# Food companies
nodes_food <- nodes_industry %>%
  filter(group == "Food Company")

# Seafood-processing companies
nodes_seafood <- nodes_industry %>%
  filter(group == "Seafood-processing Company")

# Consumer-goods companies
nodes_goods <- nodes_industry %>%
  filter(group == "Consumer-goods Company")

# Transport Logistics companies
nodes_transport <- nodes_industry %>%
  filter(group == "Transport-logistics Company")

# fishing-related companies
nodes_fishing <- nodes_industry %>%
  filter(group == "Fishing-related Company")

# multi-industry companies
nodes_multi <- nodes_industry %>%
  filter(group == "Multi-Industry Company")

II. Filter links belonging to those companies

Get links data of Individuals linked to each company in the each industry

# Overall company links
links_industry <- mc3_links_new %>%
  filter(source %in% nodes_industry$id)

# industrial companies
links_industrial <- mc3_links_new %>%
  filter(source %in% nodes_industrial$id)

# Food companies
links_food <- mc3_links_new %>%
  filter(source %in% nodes_food$id)

# Seafood-processing companies
links_seafood <- mc3_links_new %>%
  filter(source %in% nodes_seafood$id)

# Consumer-goods companies
links_goods <- mc3_links_new %>%
  filter(source %in% nodes_goods$id)

# Transport Logistics companies
links_transport <- mc3_links_new %>%
  filter(source %in% nodes_transport$id)

# fishing-related companies
links_fishing <- mc3_links_new %>%
  filter(source %in% nodes_fishing$id)

# multi-industry companies
links_multi <- mc3_links_new %>%
  filter(source %in% nodes_multi$id)

III. Get unique source and target from filtered links

# Overall Companies
links_source <- links_industry %>%
  distinct(source) %>%
  rename("id" = "source")

links_target <- links_industry %>%
  distinct(target) %>%
  rename("id" = "target")

# industrial companies
links_source1 <- links_industrial %>%
  distinct(source) %>%
  rename("id" = "source")

links_target1 <- links_industrial %>%
  distinct(target) %>%
  rename("id" = "target")

# Food companies
links_source2 <- links_food %>%
  distinct(source) %>%
  rename("id" = "source")

links_target2 <- links_food %>%
  distinct(target) %>%
  rename("id" = "target")

# Seafood-processing companies
links_source3 <- links_seafood %>%
  distinct(source) %>%
  rename("id" = "source")

links_target3 <- links_seafood %>%
  distinct(target) %>%
  rename("id" = "target")

# Consumer-goods companies
links_source4 <- links_goods %>%
  distinct(source) %>%
  rename("id" = "source")

links_target4 <- links_goods %>%
  distinct(target) %>%
  rename("id" = "target")

# Transport Logistics companies
links_source5 <- links_transport %>%
  distinct(source) %>%
  rename("id" = "source")

links_target5 <- links_transport %>%
  distinct(target) %>%
  rename("id" = "target")

# fishing-related companies
links_source6 <- links_fishing %>%
  distinct(source) %>%
  rename("id" = "source")

links_target6 <- links_fishing %>%
  distinct(target) %>%
  rename("id" = "target")

# multi-industry companies
links_source7 <- links_multi %>%
  distinct(source) %>%
  rename("id" = "source")

links_target7 <- links_multi %>%
  distinct(target) %>%
  rename("id" = "target")

IV. Bind links together to get overall nodes dataframe

# Overall Companies
nodes_industry_new <- bind_rows(links_source, links_target) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# industrial companies
nodes_industrial_new <- bind_rows(links_source1, links_target1) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# Food companies
nodes_food_new <- bind_rows(links_source2, links_target2) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# Seafood-processing companies
nodes_seafood_new <- bind_rows(links_source3, links_target3) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# Consumer-goods companies
nodes_goods_new <- bind_rows(links_source4, links_target4) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# Transport Logistics companies
nodes_transport_new <- bind_rows(links_source5, links_target5) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# fishing-related companies
nodes_fishing_new <- bind_rows(links_source6, links_target6) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

# multi-industry companies
nodes_multi_new <- bind_rows(links_source7, links_target7) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

V. Create Graph Objects

# Overall company graph
industry_graph <- tbl_graph(nodes = nodes_industry_new,
                            edges = links_industry, 
                            directed = FALSE)

# industrial companies
industrial_graph <- tbl_graph(nodes = nodes_industrial_new,
                              edges = links_industrial, 
                              directed = FALSE)

# food companies
food_graph <- tbl_graph(nodes = nodes_food_new,
                        edges = links_food, 
                        directed = FALSE)

# Seafood-processing companies
seafood_graph <- tbl_graph(nodes = nodes_seafood_new,
                          edges = links_seafood, 
                          directed = FALSE)

# Consumer-goods companies
goods_graph <- tbl_graph(nodes = nodes_goods_new,
                        edges = links_goods, 
                        directed = FALSE)

# Transport Logistics companies
transport_graph <- tbl_graph(nodes = nodes_transport_new,
                            edges = links_transport, 
                            directed = FALSE)

# fishing-related companies
fishing_graph <- tbl_graph(nodes = nodes_fishing_new,
                          edges = links_fishing, 
                          directed = FALSE)

# multi-industry companies
multi_graph <- tbl_graph(nodes = nodes_multi_new,
                          edges = links_multi, 
                          directed = FALSE)

Industrial Companies

Code

# Measure degree centrality and save as a column
V(industrial_graph)$degree <- degree(industrial_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0", 
                  "Industrial Company" = "#2980B9")

set.seed(1234)
industrial_network <- industrial_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    # Ensure that UBO and Multi-role entity nodes can be clearly seen by making node size larger
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
  # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  # Only label companies in top 25th percentile of degree measure
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Industrial Company Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

industrial_network

Food Companies

Code

# Measure degree centrality and save as a column
V(food_graph)$degree <- degree(food_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0", 
                  "Food Company" = "grey40")

set.seed(1234)
food_network <- food_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
   # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Food Industry Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

food_network

Seafood Processing Companies

Code

# Measure degree centrality and save as a column
V(seafood_graph)$degree <- degree(seafood_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0", 
                  "Seafood-processing Company" = "#B78EA4")

set.seed(1234)
seafood_network <- seafood_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
   # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Seafood-Processing Industry Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

seafood_network

Consumer Goods Companies

Code

# Measure degree centrality and save as a column
V(goods_graph)$degree <- degree(goods_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0", 
                  "Consumer-goods Company" = "#138D75")

set.seed(1234)
goods_network <- goods_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
   # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Consumer Goods Industry Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

goods_network

Transport & Logistics Companies

Code

# Measure degree centrality and save as a column
V(transport_graph)$degree <- degree(transport_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0", 
                  "Transport-logistics Company" = "#85929E")

set.seed(1234)
transport_network <- transport_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
   # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Transport and Logistics Industry Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

transport_network

Fishing-related Companies

Code

# Measure degree centrality and save as a column
V(fishing_graph)$degree <- degree(fishing_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0",  
                  "Fishing-related Company" = "#629DBB")

set.seed(1234)
fishing_network <- fishing_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
   # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Fishing-related Industry Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

fishing_network

Multi-industry Companies

Code

# Measure degree centrality and save as a column
V(multi_graph)$degree <- degree(multi_graph, mode = "all")

# Define custom colors for each group
group_colors <- c("Ultimate Beneficial Owner" = "#D86171" , 
                  "Shareholder" = "#163759", 
                  "Company Contact" = "#F39C12" , 
                  "Multi-role Entity" = "#48C9B0", 
                  "Multi-Industry Company" = "#766F87")

set.seed(1234)
multi_network <- multi_graph %>%
  ggraph(layout = "nicely") +
  geom_edge_fan(
    alpha = .6,
    show.legend = FALSE
  ) +
  scale_edge_width(
    range = c(0.1,4)
  ) +
  geom_node_point(
    aes(size = ifelse(group == "Ultimate Beneficial Owner" | group == "Multi-role Entity", 3, degree),
        color = group),
    alpha = .9
  ) +
  # Remove the legend for "degree"
  guides(color = guide_legend(title = "Role:"),
         size = "none"
  ) + 
   # Assign custom colors to each group to standardise
  scale_color_manual(values = group_colors) + 
  geom_node_text(
    aes(label = ifelse(degree > quantile(degree, .75), id, "")), 
    size = 2,
    repel = TRUE
  ) +
  labs(
    title = "Multi-industry Company Network"
  ) +
  theme(
    plot.title = element_text(size = 16,
                              color = "grey20"),
    legend.title = element_text(),
    legend.position = "bottom",
    # legend.direction = "horizontal",
    legend.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    panel.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.background = element_rect(fill="#dfdfeb",colour="#dfdfeb"),
    plot.margin = margin(r = 10,
                         l = 10)
  )

multi_network

Initial Insights from Visualisations:

• The networks of Multi-industry & Consumer Goods feature Ultimate Beneficial Owners, as well as multiple companies with large numbers of shareholders within the network structure
• There are many single-linked connections within the network structures, which suggests that the missing ‘links’ are filled with connections to companies in other industries.

4 Quantifying Network Similarity

4.1 Jaccard Similarity Index

The Jaccard similarity index compares the presence or absence of edges (links) between pairs of nodes in the networks, calculating the ratio of the number of common edges to the total number of edges in both networks. A higher Jaccard Similarity score thus suggests a greater overlap in edge connections, which makes the networks relatively more similar in terms of overall connectivity.

The measure is calculated for each pair of Industry-classified networks, with the data stored as a matrix. This is then visualised through a heatmap:

I. Define Jaccard Simmilarity Function

# Store all graph objects as a list
all_industries <- list(industrial_graph, food_graph, seafood_graph, goods_graph, transport_graph, fishing_graph, multi_graph)

# Create function to calculate jaccard similarity between two network graphs
jaccard_sim <- function(graph1, graph2) {
  common_edges <- intersect(E(graph1), E(graph2))
  jaccard_similarity <- length(common_edges) / (length(E(graph1)) + length(E(graph2)) - length(common_edges))
  return(jaccard_similarity)
}

II. Loop through each Network Graph pair

# Loop through all network graphs and calculate jaccard similarity
jaccard_similarity <- matrix(0, nrow = length(all_industries), ncol = length(all_industries))
for (i in 1:length(all_industries)) {
  for (j in 1:length(all_industries)) {
    jaccard_similarity[i, j] <- jaccard_sim(all_industries[[i]], all_industries[[j]])
  }
}

III. Create Jaccard Similarity Matrix

# Create dataframe with jaccard similarity matrix
jaccard_similarity_df <- as.data.frame(jaccard_similarity)

# Assign row and column names according to industry
network_titles <- c("Industrial", "Food", "Seafood", "Goods", "Transport", "Fishing", "Multi-industry")
rownames(jaccard_similarity_df) <- network_titles
colnames(jaccard_similarity_df) <- network_titles

kbl(jaccard_similarity_df,
    caption = "Jaccard Similarity Index Comparing Network Graphs") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Jaccard Similarity Index Comparing Network Graphs

	Industrial	Food	Seafood	Goods	Transport	Fishing	Multi-industry
Industrial	1.0000000	0.6138614	0.9462366	0.7294118	0.8017241	0.9731183	0.7209302
Food	0.6138614	1.0000000	0.5808581	0.8415842	0.7656766	0.5973597	0.8514851
Seafood	0.9462366	0.5808581	1.0000000	0.6901961	0.7586207	0.9723757	0.6821705
Goods	0.7294118	0.8415842	0.6901961	1.0000000	0.9098039	0.7098039	0.9883721
Transport	0.8017241	0.7656766	0.7586207	0.9098039	1.0000000	0.7801724	0.8992248
Fishing	0.9731183	0.5973597	0.9723757	0.7098039	0.7801724	1.0000000	0.7015504
Multi-industry	0.7209302	0.8514851	0.6821705	0.9883721	0.8992248	0.7015504	1.0000000

Code

# Visualize the Jaccard similarity matrix as a heatmap
pacman::p_load(heatmaply)
heatmaply(as.matrix(jaccard_similarity_df),
          colors = Blues,
          seriate = "OLO",
          main="Jaccard Similarity Scores Comparing Industry Networks",
          margins = c(NA,200,60,NA)
          ) %>%
  plotly::layout(plot_bgcolor = "#dfdfeb",
                paper_bgcolor = "#dfdfeb")

4.2 Orbit Distribution Agreement (ODA)

This measure provides a way to compare the local structural patterns between networks, focusing on the distribution of orbit types. It captures the similarity or dissimilarity in the occurrence of specific subgraph patterns within the network, which can provide insights into their functional properties, evolution, or dynamics.

I. Define Structural Equivalence Function

# Store all graph objects as a list
all_industries <- list(industrial_graph, food_graph, seafood_graph, goods_graph, transport_graph, fishing_graph, multi_graph)

# Create function to calculate oda between two network graphs
net_oda <- function(graph1, graph2) {
  calc <- netODA(graph1, graph2)
  return(calc)
}

II. Loop through each Network Graph pair

# Loop through all network graphs and calculate net ODA
netOda_matrix <- matrix(0, nrow = length(all_industries), ncol = length(all_industries))
for (i in 1:length(all_industries)) {
  for (j in 1:length(all_industries)) {
    netOda_matrix[i, j] <- net_oda(all_industries[[i]], all_industries[[j]])
  }
}

III. Create net ODA Matrix

# Create dataframe with jaccard similarity matrix
netOda_df <- as.data.frame(netOda_matrix)

# Assign row and column names according to industry
rownames(netOda_df) <- network_titles
colnames(netOda_df) <- network_titles

kbl(netOda_df,
    caption = "Orbit Distribution Agreement of Network Graphs") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Orbit Distribution Agreement of Network Graphs

	Industrial	Food	Seafood	Goods	Transport	Fishing	Multi-industry
Industrial	1.0000000	0.9061240	0.9733774	0.8688935	0.9069822	0.9886228	0.8644830
Food	0.9061240	1.0000000	0.9015054	0.8491589	0.8940284	0.9127471	0.8574863
Seafood	0.9733774	0.9015054	1.0000000	0.8503657	0.8996167	0.9820156	0.8691342
Goods	0.8688935	0.8491589	0.8503657	1.0000000	0.8497719	0.8661265	0.8064378
Transport	0.9069822	0.8940284	0.8996167	0.8497719	1.0000000	0.9126474	0.8539392
Fishing	0.9886228	0.9127471	0.9820156	0.8661265	0.9126474	1.0000000	0.8730766
Multi-industry	0.8644830	0.8574863	0.8691342	0.8064378	0.8539392	0.8730766	1.0000000

Code

# Visualize the net ODA matrix as a heatmap
heatmaply(as.matrix(netOda_df),
          colors = Greens,
          seriate = "OLO",
          main="Orbit Distribution Agreement Across Industry Networks",
          margins = c(NA,200,60,NA)
          ) %>%
  plotly::layout(plot_bgcolor = "#dfdfeb",
                paper_bgcolor = "#dfdfeb")

Similarities in Network Structures Across Industries:

Similarities in Network structure can be derived from both Jaccard Similarity (JS) and Orbit Distribution Agreement (ODA) scores presented in the heatmap. The lighter the tile, the less similar these ‘groups’ are.

• Highly similar industries:
  • Fishing & Seafood-processing Industries have the very high JS and ODA index of nearly 1.0, suggestive of almost identical network structures.
  • Seafood & Industrial and Fishing & Industrial structures also scored nearly 1.0 for both similarity measures. This suggests that these industries may be overlapping, highly related or interconnected with each other.

5 A Fishy Network?

Many of the ‘groups’ of companies by industry had high jaccard similarity and Orbit Distribution scores, which suggests that there is a significant overlap in the connections and relationships between companies, owners, and contacts involved in the overall network between industries. These network diagrams are visualised to compare the graphlet structures within the expanded networks, to determine if there are fishy ties between entities across industries.

In particular, networks with high Jaccard similarity could indicate:

• Collaboration and Cooperation between industries
• Cross-Industry Syndicates that span across multiple industries, coordinating illegal fishing activities and exploiting resources collectively.
• Common entities who benefit from illegal fishing practices

A high Orbit Distribution Agreement could also suggest:

• Common Network Dynamics or organizational structures. This could involve shared roles, relationships, or coordination mechanisms among key actors involved in illegal fishing.
• Coordinated Activities between industries

As such, the following subgroups of networks are visualised for further comparison:

5.1 Industrial, Fishing-related and Seafood-processing Companies

Code

# industrial, fishing and seafood companies
nodes_fishy <- nodes_industry %>%
  filter(group =="Industrial Company" | group =="Fishing-related Company" | group =="Seafood-processing Company")

# Overall links
links_fishy <- mc3_links_new %>%
  filter(source %in% nodes_fishy$id)

# Overall nodes from links data
links_fishy_source <- links_fishy %>%
  distinct(source) %>%
  rename("id" = "source")

links_fishy_target <- links_fishy %>%
  distinct(target) %>%
  rename("id" = "target")

# Overall Companies
nodes_fishy_new <- bind_rows(links_fishy_source, links_fishy_target) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

links_fishy_new <- links_fishy %>%
  rename("from" = "source",
         "to" = "target")

Code

net5 <-
  visNetwork(
    nodes_fishy_new, 
    links_fishy_new,
    width = "100%",
    main = list(text = "Fishing, Seafood and Industrial Company Networks",
                style = "font-size:17x;
                weight:bold;
                text-align:right;")
  ) %>%
  visIgraphLayout(
    layout = "layout_with_fr"
  ) %>%
  visGroups(groupname = "Industrial Company",
            shape = "icon",
            icon = list(code = "f013",
                        size = 45,
                        color = "#51585D")) %>%
  visGroups(groupname = "Seafood-processing Company",
            shape = "icon",
            icon = list(code = "f578",
                        size = 45,
                        color = "#B78EA4")) %>%
  visGroups(groupname = "Fishing-related Company",
            shape = "icon",
            icon = list(code = "f21a",
                        size = 45,
                        color = "#52DFD6")) %>%
  visGroups(groupname = "Ultimate Beneficial Owner",
            color = "#D86171") %>% 
  visGroups(groupname = "Shareholder",
            color = "#247EBF") %>% 
  visGroups(groupname = "Company Contact",
            color = "#F39C12") %>% 
  visGroups(groupname = "Multi-role Entity",
            color = "#48C9B0") %>% 
  visLegend() %>%
  visEdges() %>%
  addFontAwesome() %>%
  visOptions(
  # Specify additional Interactive Elements
    highlightNearest = list(enabled = T, degree = 2, hover = T),
  # Add drop-down menu to filter by company name
    nodesIdSelection = TRUE,
  # Add drop-down menu to filter by category
    selectedBy = "group",
    collapse = TRUE) %>%
  visInteraction(navigationButtons = TRUE)

net5

Fishing, Seafood and Industrial Company Networks

Select by idAdriatic Squid Ltd. Liability CoAdriatic Tuna Seabass BV TransitAndhra Pradesh Tidal flat Pic ExpressAqua Aura SE Marine lifeAqua Ventures Sp OverseasArabian Sea Sp ExpressBenton Limited Liability CompanyArunachal Pradesh s AS Marine sanctuaryArunachal Pradesh s Sagl TransportAssam Limited Liability CompanyBahía Azul Aquamarine Corporation and Son'sBahía de Coral Ltd. Corporation TransportBahía de Plata ASBalkan Anchovy Sp HoldingsBalkan Trout S.A. de C.V.Baltic Sprat Incorporated InvestmentBarco de Plata IncorporatedBarents Sea Cod - LC UnitedBarents Sea Shrimp OJSC _Black Sea Anchovy Oyj TransportBlue Horizon AB WorldwideBlue Water Ventures Marine lifeBrisa Marina CJSC BonitoBrisa del Mar Pic MarineYenisei Eel GmbH & Co. KG ServicesBu yu wang SpBujagali Falls Tidal flat A/S FreightCape of Good Hope AG ExpressCoastal Castaway Sp HerringCoral Cove Ltd SolutionsCoral Cove Marine mist LC ExportCoral del Sur Limited Liability Company ConsultantsCosta de Oro S.p.A.Delhi Ltd. Liability CoDelhi ers Express LockDiao yu BV LogisticsDoris Ltd. Corporation MerchantsDry Creek CJSC MarineDutch Mussels S.A. de C.V. LineEstrella de Mar LLC ShippingEstrella de Mar N.V.Estrella del Mar ОАО TransitEstrella del Sol Sagl CargoEukrante GmbH & Co. KG LogisticsFaroe Islands Shrimp Inc ShippingFin Flair ОАО CarriersFinest Dredge SRL CargoFisher Marine conservation CJSC TransportationFisher S.A. de C.V.Ghetla Ltd. Liability CoGreek Sea Bream Bonito LC BrothersGroup and Son's Dry dockHarbah Motorboat AG ShippingHaryana Sea Marine ecologyHaryana s Catchers ОАО EnterprisesHenan Ltd. Liability CoIrish Crab Sp ServicesIrish Mackerel S.A. de C.V. Marine biologyIrish Sea Incorporated TransitIsla del Mar BVIsla del Mar Ltd. Liability Co EnterprisesJammu & Kashmir Sea Ltd InvestmentKarelians Plc Marine conservationKarnataka s CJSC WorldwideKhundu N.V. and Son'sKilimanjaro fishery Kga NauticalLake Kainji S.p.A. ExportLake Turkana Marine sanctuary NV and Son'sLimpopo River AB ConsultantsMadhya Pradesh Market Marine mistMadhya Pradesh Market Shoal GmbH ShippingMar Azul Sp PelicanMar de Plata S.A. de C.V. Marine conservationMar de la Aventura TidepoolMar de la Vida CompanyMar del Norte NVMar del Oeste N.V.Mar del Paraíso Ltd. CorporationMar y Luna CompanyMar y Luna KgaMar y Viento Co ShippingMarina Oceanography Limited Liability Company DeliveryNagaland Sea Catch ОАО LineNeptune's Bounty LLC InternationalNeptune's Harvest Ltd. CorporationNeptune's Realm Ltd. CorporationNeva S.A. de C.V. ShippingManipur Market Ltd. Liability CoNorwegian King Crab Corporation WorldwideNáutica del Mar LCOb River Ltd ForwadingOcean Blue OJSC ForwadingOcean Odyssey & Kga TransportOceanFront Foods Limited Liability Company Marine lifeOceanfront Oases - Ltd. Liability Co Marine lifeOceanicHarvest GmbH & Co. KG TideOla de la Costa LLCPerla del Mar CoPerla del Mar WhalePlaya de Arena & Son'sPlaya de Arena S.A. de C.V.Playa de Coral Hijiki OJSC TransitPlaya de la Luna Seaport LLC ExpressPlaya del Cielo KgaPlaya del Mar N.V. SeafoodPlaya del Sol ASPortuguese Sardines Ltd. Corporation BuoyPortuguese Sea Bass JetsamRajasthan Market Limited Liability CompanyRift Valley fishery KgaSaharan Coast Pic ForwadingSaltwater Serenity Coral Reef Corporation LineSaltwater Sisters SRL CargoSaltwater Solitude OJSC TransportSamaka Chart ОАО DeliverySamaka Ltd. Liability Co DistributionScania Ltd. Liability CoScottish Herring & Son'sSea Breezes Sagl SeasideSea Harvest CJSC Marine sanctuarySea Harvest Plc TransitSea Spray GmbH & Co. KG ServicesSea Spray Starfish N.V. OverseasSea Striders Kelp BV SolutionsSeaSiren Beachcomber AG TransportSeaSolutions Leska Kga WorldwideSeaSplash Foods Ltd. Liability Co ExpressSeashell Sanctum Co FamilySeashell s Limited Liability Company BuoySeashore Sanctuary Marine lifeSeaside Summit Tidal Incorporated LineSolutions Limited Liability CompanySpanish Red Snapper Current Ges.m.b.H. LineThe Salted Pearl - Oyj Marine conservationThe Salty Pearl PicThe Sea Lion N.V. CargoThe Sea Turtle GmbH & Co. KG FreightTiao piao Limited Liability CompanyTiao piao Ltd. Liability CoTransit Limited Liability CompanyUttar Pradesh s Ges.m.b.H.Vespuci Limited Liability CompanyViento y Mar BVViento y Mar LLC ExportVolga River LC ServicesWater Wonders Limited Liability CompanyWater World Ltd. Liability Co BrothersWest Bengal A/S ImportWest Bengal Ltd. CorporationYu er he Seabái suō wěn lú CJSCZnamensk Ltd. Liability CoKallianassa Fish AG Solutionsguī yú N.V. Catcherssuō yú Ltd. Liability CoAngelica WheelerShelly StrongMatthew MoraRyan StanleyTina WilsonVernon CoxMatthew HillJennifer BauerRachel SanfordAlan TateEduardo NavarroJack SharpRichard GilbertSharon JeffersonStephanie ContrerasJennifer ScottKaylee MartinezSamantha HillMichelle JonesEdward BryantDavid GoldenWilliam FrederickJennifer KerrKeith BirdMarcus BullockMichael BushSamantha StevensonThomas BrooksYolanda BernardMrs. Crystal Miller MDHeather KnightAdam JohnsonAllison SampsonCharles HillChris MedinaDavid VillarrealDavid WilliamsonDeborah HuangJason GoodwinMrs. Kelly KimRichard YoungSharon HarringtonStephen BaileyAndrea PotterElizabeth DillonSharon WallaceJose RamirezBeth HarveyBeverly BentonBrittany NelsonKayla JamesPaige BeckerDawn StewartBailey BruceJoseph JohnsonMichael RichardsonMatthew FreemanStephanie WalkerPeter RamirezAlex GomezLori WilsonStephen RiggsAndrew HouseCameron SantosDennis JosephDillon GuzmanFrederick ConwayJonathan FergusonJoshua AtkinsJustin DuncanLeslie HaleMatthew JohnsonMelody GlassNathan PetersonSeth ScottShawn TannerStefanie RoseTonya MullinsJack MorganJodi GarciaKelly MclaughlinCrystal HillJoseph LynchMatthew JoyceMelody HarrisPaul EricksonMaria BakerPaula JohnsonPaul MyersTaylor GonzalezMeghan LuceroDaniel SanfordRonald CarterDr. Cheryl AndersonJack WhiteJeff ContrerasJohn WilsonJose ParkLinda MillerLouis ThomasMonica FrazierNicholas WilliamsNicole LewisRandall SmithTeresa ReeseTimothy NunezTrevor DeanCurtis MunozDr. Donald GeorgeMonica Johns MDRobert CarpenterBrandy WashingtonSherry LindseyAllen WigginsColton MyersDerek JonesHeather EllisLaura MaddoxHeather Chandler MDAdam StevensMarisa BenderDeborah TaylorAmy GonzalezCasey WilliamsDamon ChandlerDaniel AbbottMichael CooperMichael PowellNicole SuttonRoger NelsonWendy CarterAlexander SmithJoshua WilliamsAlyssa NicholsAnthony BurnsAnthony HarrisAntonio MitchellAshley AdamsConnor ThompsonCynthia TorresDiamond MendozaDr. James Anderson DVMJamie HughesJason GarrettJohn HuynhKathryn TranKevin SmithKirk ThorntonMark Stephens DVMMelanie FoxMelinda WardMelody GilmoreNatalie WhitakerPatricia EdwardsPenny CastanedaRobert AllenScott TorresSharon LuceroTeresa RoblesJerry DanielJessica HolmesKevin BanksKim WilcoxLaurie HutchinsonLindsay WalshRachel ClarkePatrick AllenCynthia LeeWilliam KimBonnie WhiteRichard ConnerRodney HornSpencer ThompsonDouglas HendersonJennifer PattersonJeremy JacobsMelissa DavisRandy JohnsonAlison MoralesAmy PayneAshley RossBrett AndersonChristopher MillerColleen VangDavid NelsonDonna HernandezGeorge BurkeGrant HensonJeffery JonesJeffrey BuchananJeremy MathisJermaine MendezJonathan PittmanJustin AndersonJustin BrightKara GutierrezLarry DoughertyMarvin BoydMelanie BrownMelissa ThomasMichael FoleyMrs. Cynthia AvilaPatrick BirdRachel ReidRebecca CaldwellRobert CookSteven VasquezSusan MeadowsVincent SingletonJeffrey PachecoMike ClarkBrittany EdwardsJonathan OwensCynthia MurphyDale RhodesJillian WhiteLeah CruzLuis MatthewsJustin LopezVanessa GilbertThomas DavidsonKayla ThompsonAnthony JacobsonBrian WatsonDouglas JensenJaclyn BarnesJames LeachJohn WilliamsJoseph LoveLisa MontgomeryMelissa JuarezMichael BennettRandy SolisRebecca MurrayRichard HunterRyan ColeSean KimSheena RodgersSherri HoffmanWendy ThomasWilliam JensenLeroy JacobsAmy MartinezSherry HuntDouglas GreeneJennifer VillarrealTiffany BrownWilliam LeeKelly FreemanClaudia Watson DVMVeronica ParkerCorey DrakeElizabeth CoxHunter RamirezLisa BellBrian LozanoAutumn TrujilloBrandon RoseRose MccallCrystal PriceHaley JensenHenry OliverMaria MartinPamela WhiteRobert IbarraSheena AlvaradoDavid MorrisonKimberly RodriguezKenneth JohnsonRyan CollinsDarrell HuffmanDouglas MoralesAshley MendezElizabeth BakerLuis CampbellNicole ParsonsRobert KramerTiffany SolomonZachary LeonBenjamin GentryBryan AllenChristine WhiteGabriela YorkJennifer LloydLori PatrickJessica MorrisAlexander HooverNicholas RussellDale CantrellChristopher BrayGabriel FreemanSarah WilliamsKyle GarciaRichard SharpJames MeyerLori NicholsonAnn EllisDeborah OlsonEmily WilliamsJessica NorrisJohn Smith DDSMary BarrettMary MorrisonPamela ClarkRichard GlassRonnie BarrettShaun PaulLauren RiddleAngela MasseyCharles WilkinsonDr. Terry BarnesJoshua HarperKatherine ValentineKurt MasonMichelle Grant DDSMichelle RichmondRyan Martin DVMZachary CampbellJason GilbertOlivia JohnsonMatthew OlsenAshley SweeneyHeather WilsonMaria StoutKathleen GreenPamela WilliamsAlicia HullCharles Guzman Jr.Christopher MatthewsJacqueline ColeJanet GordonJesse WilsonPatricia LeeRachel SmithSandra GarciaSusan WheelerTraci BlanchardAlan BoydChase TaylorChristopher PetersEdward StaffordJacqueline DixonJason RodriguezJoel SanchezJoshua BarkerKristi KellerLeah SweeneyMarc MccoyMichael MurrayMichael WrightNicole HendersonStephen JohnsonThomas RomanTyler VegaWilliam JohnsonKaren WilsonKimberly JimenezStacey WilliamsonLaura BryantRonald BrownDavid IbarraJasmine McgrathLarry HickmanNatasha SuttonGeorge ChanRachel ButlerJohn NewmanAmanda MarquezStephanie QuinnTammy FieldsTimothy ParkerHeather NunezKelly SanchezAaron CunninghamDennis MontgomeryDr. Glenn Newman MDFernando WatersLaura ShafferScott HubbardAlexandria MarquezMichael GonzalesMegan OsborneDanielle LopezTammie SmithAnita WaltonAnthony DavidJohn WalkerTammy KellerElizabeth ReidEmily HodgesRoger ChapmanMatthew HarrisErika GilbertNathan MarquezElizabeth StephensRonald SanchezLance HaynesRobert ThomasTony MasonDennis ShermanNatalie KennedyBrian YangLeonard NelsonJames StevensonJennifer TaylorLouis OconnellScott PerezBrian ThomasChristopher WhiteConnor PerryDavid MossKimberly WillisPatricia SpencerRonald WestStephanie DeanTracey SmithDennis StewartShannon SotoAmanda KingRose RamseyBeverly MooreSean BaxterMelody SkinnerJonathan WilliamsNicholas LangRicardo ThomasRobert PhillipsJoanna CruzMichael MarshallPeter RamosAlexander ReeseSandra FoxShane MortonAlex BradleyCarolyn PhillipsCharles MartinChristopher TerryCole NewtonDaniel SmithHayley MullenJames GonzalezJennifer YoungKeith PierceKenneth PerryKim DavisKimberly WilliamsMark DavisRicky SmithSarah BurkeShannon JensenCynthia FieldsCynthia WilkersonJoel BennettKyle ChristensenMark WarnerMatthew JonesRebecca CrawfordRobert MartinezTina BrooksTyler EdwardsShannon WilliamsDanielle ColeCatherine HoffmanJames AndersonMartin YoungMr. Jared Peterson IIRyan EdwardsAdam SmithPatrick LongVictoria FreemanKimberly OrtizAngela MooreJames PalmerMelanie AllenSarah CrawfordEllen GonzalezChad BlevinsJohn BurnsMarcus HarrisTeresa LinHannah ReyesAllen GriffinChristopher JimenezGeorge BrownJohn DavisSean StewartChristine HammondJennifer GuerraDavid HughesJustin Sullivan Jr.Lisa JamesMary WiseKayla AdamsAmanda MitchellAmanda MyersAnthony AtkinsonBrian SmithBrittany DunnCarrie DoyleCindy UnderwoodEdward MitchellEmily GrantErica OrtizGabriella RodriguezJames JonesJessica KaneJohn RogersJoseph BooneJuan OlsonLarry StevensMichael SnyderNatalie LamRussell PowersSabrina GarciaSavannah WebbStephen GravesTeresa RodriguezVincent ElliottJonathan GonzalesGabriel AustinJenna RiveraAshley ShermanCarly LaneDr. Matthew ChanGary OconnorJason CaldwellJohn KingKimberly WhiteMaria JonesMichael FloresMichelle ToddTyrone NelsonZachary FarrellZachary LewisAlexandra Smith

---

Select by groupCompany ContactFishing-related CompanyIndustrial CompanyMulti-role EntitySeafood-processing CompanyShareholderUltimate Beneficial Owner

5.2 Consumer Goods and Transport-logistics Industries

Code

# Consumer Goods and Transport Industries
nodes_fishy2 <- nodes_industry %>%
  filter(group =="Consumer-goods Company" | group =="Transport-logistics Company")

# Overall links
links_fishy2 <- mc3_links_new %>%
  filter(source %in% nodes_fishy2$id)

# Overall nodes from links data
links_fishy2_source <- links_fishy2 %>%
  distinct(source) %>%
  rename("id" = "source")

links_fishy2_target <- links_fishy2 %>%
  distinct(target) %>%
  rename("id" = "target")

# Overall Companies
nodes_fishy2_new <- bind_rows(links_fishy2_source, links_fishy2_target) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

links_fishy2_new <- links_fishy2 %>%
  rename("from" = "source",
         "to" = "target")

Code

net6 <-
  visNetwork(
    nodes_fishy2_new, 
    links_fishy2_new,
    width = "100%",
    main = list(text = "Consumer-goods and Transport-logistics Networks",
                style = "font-size:17x;
                weight:bold;
                text-align:right;")
  ) %>%
  visIgraphLayout(
    layout = "layout_with_fr"
  ) %>%
  visGroups(groupname = "Consumer-goods Company",
            shape = "icon",
            icon = list(code = "f291",
                        size = 45,
                        color = "#138D75")) %>%
  visGroups(groupname = "Transport-logistics Company",
            shape = "icon",
            icon = list(code = "f4df",
                        size = 45,
                        color = "#85929E")) %>%
  visGroups(groupname = "Ultimate Beneficial Owner",
            color = "#D86171") %>% 
  visGroups(groupname = "Shareholder",
            color = "#247EBF") %>% 
  visGroups(groupname = "Company Contact",
            color = "#F39C12") %>% 
  visGroups(groupname = "Multi-role Entity",
            color = "#48C9B0") %>% 
  visLegend() %>%
  visEdges() %>%
  addFontAwesome() %>%
  visOptions(
  # Specify additional Interactive Elements
    highlightNearest = list(enabled = T, degree = 2, hover = T),
  # Add drop-down menu to filter by company name
    nodesIdSelection = TRUE,
  # Add drop-down menu to filter by category
    selectedBy = "group",
    collapse = TRUE) %>%
  visInteraction(navigationButtons = TRUE)

net6

Consumer-goods and Transport-logistics Networks

Select by id5 Limited Liability CompanyAncla del Este LCAquatic Adventures GmbH & Co. KGArena del Sol SEArunachal Pradesh s Kga OverseasArunachal Pradesh s PlcAsiotr Ltd. Liability Co TransitBalkan Trout Ltd. Corporation TransitBaltic Salmon Corp Marine lifeBarco de Plata SaglBarents Sea Shrimp LC MarineBelgian Scallop AG SeagrassBelgian Shrimps Oyj BrothersBig Sulphur Company DeliveryBlue Water Ventures Limited Liability CompanyBlue Water Ventures Sea GmbH ConsultingBrisa Marina OJSC FreightCape Verde Islands PelicanCape of Good Hope BVChobe River and Son'sCoastalCrave S.A. de C.V. SolutionsCongo Rapids Ltd. CorporationCoral Kingdom NV SolutionsCosta de Oro CompanyCosta del Sol S.p.A.Dutch Bill A/S DistributionDutch Oyster Shipping LeskaElara Mussel AS Marine biologyEstrella del Mar Ges.m.b.H.Eukrante Marine biology A/S ExpressFan er ОАО DeliveryFlint Seagull OJSC HoldingsFrench Sole AB LighthouseGreat Ruaha River OceanfrontGreek Octopus Dredge SE ForwadingGreek Octopus Limited Liability CompanyGreek ermen Kga FreightGreen Valley Marine biology KelpHaryana s A/S CargoHaryana s Limited Liability Company SolutionsHenties Bay Ltd. Corporation FamilyIrish Trout BV LogisticsItalian Anchovy LC InternationalJammu & Kashmir AB ConsultantsJammu & Kashmir Sea Cod SRL CargoKambala Coral Co ShippingKambala Ltd. Liability CoKerala Market Oyj FreightKong zhong diao yu Ges.m.b.H.Lake Chad Limited Liability Company ExportLake Malawi Corp UnitedLake Tanganyika Nori LLC BrothersLake Victoria Catch GmbHPlaya de la Luna CJSCLine S.A. de C.V.Ltd. Corporation Ltd. CorporationLtd. Liability Co SRLMaharashtra GmbH & Co. KGManipur Market Sp ForwadingMar de Cristal Roe NV ShippingMar de Cristal S.A. de C.V.Mar de Estrellas A/S CarriersMar de Estrellas Ges.m.b.H.Mar de Plata YachtMar del Golfo OJSCMar del Paraíso ОАОMarine Muse Pic Marine ecologyNagaland Sea Catch AB SolutionsNiger River Delta Inc MarineNiger River Delta S.p.A.Norwegian King Crab BVNáutica del Sol Ges.m.b.H.Náutica del Sol Seafarer Company TransitOOO Ltd. Liability CoOcean Blue Ges.m.b.H. OverseasOcean Emporium LLC _OceanGem Flotsam GmbH & Co. KG DeliveryOceanfront Oasis GmbH & Co. KG CarriersOceanic Explorers Plc Salt sprayOceanicOrigin Foods Cargo ship Sagl ShippingOla Azul Ltd. Liability CoOla del Mar PlcOlas del Mar LtdOlas del Mar Oyj EnterprisesOlas del Mar WorldwideOrange River Mouth BVOranjestad Ltd. Liability CoPerla del Mar Corporation and Son'sPerla del Mar SashimiPlaya Azul Ltd CargoPlaya de Coral Oyj ImportPlaya de Oro BVPlaya de Oro Inc UnitedPlaya de Oro ОАО LockPlaya de la Felicidad Sagl TrawlerPlaya del Cielo Sextant LC FamilyRajasthan Market Ltd. Liability CoReefRider Foods ОАО ShippingRichard GmbH & Co. KGSahara LLC LC MerchantsSaharan Coast Ges.m.b.H. ShippingSailors and Surfers ОАО DeliverySaltSea Ltd. Corporation Marine sanctuarySaltwater Sanctuary Worldwide LaksaSaltwater Sisters Ges.m.b.H. TankerSaltwater Surf Club AB BrothersSavaneta & AB TransitScottish Lobster Sextant NV ExpressScottish Trout Pic SolutionsSea Star Company SandbarSea Star Ltd. Liability CoSea Striders BV ShippingSeaBass & N.V. Marine ecologySeaBite Marina Oyj LineSeaBounty Tidepool AG MerchantsSeaSiren GmbH Marine ecologySeashore Sanctuary S.p.A. MarineSeashore Sanctuary Salt spraySenegal Coast Dredge NV TransitSenegal Coast Inc CargoSerengeti Inc. America A/S LineSoutheast Ltd. Liability Co InvestmentSpanish Anchovy GmbH & Co. KGSurf's Up s N.V.Tamil Nadu LC Marine lifeTamil Nadu Market Ltd. CorporationThe Salty Dog DistributionThe Sea Turtle Seagull Kga LineTiao piao Ltd. CorporationTideTable SRL Marine sanctuaryTunaTreat Surf Inc ExpressTurkish Mussels Tsunami Ltd ServicesTurkish Sword N.V. LogisticsUttarakhand s Plc ForwadingVictoria Falls Limited Liability CompanyViento Azul Incorporated ForwadingVleikurper Ltd. Liability CoWater World GmbH & Co. KGWater World Marine conservationWest _ GmbH & Co. KG Transitfēi yú Express Aquaguī yú Kga Cargojīng Coral LLC Carriersnián yú Jetty SE Forwadingshí rén yú BargeIsla del Este Ltdshí rén yú Marine lifesuō yú Breakwater Oyj and Son'sxún yú AB Consultantsxún yú Inc Linexún yú Incorporated and Son'sBrittany JonesElizabeth TorresSandra RobertsElizabeth WhiteThomas ArmstrongJames ScottNancy JohnsonRandy McmahonTracy NguyenDennis MaldonadoDr. Jessica CarterWayne GentryAmy MurphyJackie FrederickJeremiah DiazMegan WilliamsAdam EvansAnne AllenChristopher HenryMary OrtizSteven NobleLisa HouseMiss Jessica CollinsBrian ChurchJose MoyerRobert BennettJohn FisherChristopher EspinozaGregory WilliamsMatthew LeeStephanie WatkinsMrs. Stacie Morton DDSEdward PattonEric MartinezAntonio SnowDeborah GonzalezAmanda ZimmermanAnthony SotoCarl HayesDaniel MartinezKrystal AndradeRobert Brown Jr.Sarah GonzalesStacy GrayStephanie BrooksTabitha YuTheresa JacksonThomas PerezMark BurnsThomas HicksBrady MaynardGary HoffmanJustin HernandezMackenzie WarnerRobert GriffinTiffany MossZachary SheppardHaley PhillipsMark ArellanoMichele RobinsonPhillip GarciaChristina WilliamsRebecca KirkStephanie BrownSamantha WangAaron ParkerDaniel HarrisJesse PerezMrs. Jessica Krause MDRenee WattsRicky ClarkPeter MaddoxArthur SchwartzCarol Anderson DDSCheryl HoltCory KingElaine DanielsJane LaneKarina Martinez MDMadeline JensenMiguel RobinsonSteven GreenTravis AllenJeffrey TownsendRobert BallardJames ClarkJulie PorterThomas MolinaDaniel RodriguezJason CardenasBenjamin GlassAdrian BlevinsAshley PittsJennifer GarrisonJulian McclainMr. Hunter RoyAngela PerezChristine LindseyAnthony CastroChristopher EdwardsMary MillerJessica HowardZachary MartinJessica MoorePeggy BuchananAmy KellyTracy MarshallValerie WintersJohn GainesJohn SmithAustin BaileyCalvin CoxJacqueline MyersJoseph SmithMichael Edwards DDSStephanie CrawfordJordan CruzAlyssa VaughnDavid HawkinsDawn ManningDonald ParkerEmily FrancoHeather WrightRhonda McdonaldMichael EwingDouglas SimpsonJean PachecoJonathan GonzalezKaitlyn YangSabrina EdwardsGregory KennedyBarbara GarrettCarl StanleyChristian AshleyDonald AdamsJohn MonroeJohn WebbJustin FieldsKathryn WadeLance JohnsonMartin JohnsonMaurice JonesWilliam MitchellWilliam SandersAdam VargasAngel KingApril HooperAshley HolmesAustin DavisBrian WashingtonBrittany HernandezChristine IrwinConnie Gomez DDSDaniel DanielsDebra StephensonDonna HollowayHeather MccannHeather PatrickJack SantiagoJames ObrienJermaine BauerLance CampbellMichael BellMichael StoneRebecca JonesSally DodsonSamantha KennedySean HamiltonStephanie JohnsonTanya HartTaylor CunninghamTracey BellWesley ValenzuelaJoseph BirdRebecca PaulDavid MendezRobin ClarkeStephanie PetersonJames SosaTimothy HarveyAlexandria SmithAmanda HaleAmanda MarshAnna DavisAshley HarrisBrent LucasBryce SmithChristopher AnthonyDr. Keith BishopDr. Sean RossEric FosterHailey MirandaJeffrey MendezJennifer HillJoseph SotoLisa LopezMark JacksonMatthew LopezMelissa RussellMichele CastilloMonica ChaseNicholas OchoaPhillip HallReginald FrederickRichard DeanSteven ByrdTina LoweryZachary MartinezKatie BurnettAnthony BakerBrian FoxBrittany IngramEric NewmanGary SmithKaitlyn CookMelissa WrightMichelle MillerRonnie GreeneSteve FosterSarah BuchananSelena SwansonAngela BoydDenise CookJacob BrownJose WilliamsDavid ThomasAmy WongAngela TaylorKelly HarrisonCole AllenShawn MyersAnna KruegerBeth WardBethany MoonBrittany RussellChristine LynchCrystal McknightDesiree TaylorDevin LarsenDustin FerrellGeorge ThomasHeather SalinasJames HansenJeffrey CarterJim LivingstonJose BartonJustin MontgomeryKevin GarciaKristina MoranMatthew HinesRaymond HillRhonda MartinezRyan MartinStephanie HaydenSuzanne HowellYolanda MunozAngela WoodSharon MoralesCharles Luna IIDanielle AndersonMartin WhitakerSandra AdamsTanner JacksonCharlene ElliottTerry HartIsaac PhamLauren BurnettClinton PowersLauren KimNatalie BoyleRicky OconnellBeth HansonJeffrey BallTiffany WiseBreanna GravesEric BatesSteven HenryJessica MayDr. Charles RochaEmily BaileyAndrea WadeAlexander RichmondDavid SandovalJerry JohnsonKevin AustinKevin BirdLaura WilliamsLisa AguilarNatalie DunnPeter ThompsonRoy BrownSean HollandShane ClarkTom BrownBrittney PhillipsKatrina StoneMadison EnglishMary McdonaldIan SwansonTimothy BakerBarbara GlennKathleen MeyerLaura MooreRichard WarrenAlicia JordanJessica MclaughlinStephanie MasonMrs. Dana Reid DVMAlison ShafferJustin Robles DDSMichael DixonJames PhillipsDaniel ScottAmber PhillipsJames MedinaKathleen GordonMary MedinaDaniel ChavezIsabel HolmesJoe RhodesSusan LewisAndrea BrownJohn LarsenTammie WoodMaria HarrisAbigail RobinsonCynthia OrtizDr. Justin PrestonKristine RuizNicole OsbornRuben NobleAlexander MorrisSue RomeroKevin ChurchLaura MartinezRonald Carey IIIDaniel MoranHannah EvansJennifer JohnsonKenneth WilsonKevin HamiltonStephanie UnderwoodMiguel TorresCarlos RichmondMr. Brandon AndersonSusan RhodesHannah LopezSara WrightAmanda Nelson MDBeverly MooreSean BaxterTonya PenaNatalie JensenMike NewtonDr. Jimmy Lee IILaura DavisSteven JonesChristine NewtonHannah ThomasSamuel BanksBrooke YoungCaleb PooleDominique BatesJennifer LambertKaren MillerMichael ReidMr. Steven HowardSamuel WilliamsWillie GardnerChristina StevensonCindy SmithTanya DoyleTimothy ThompsonDana PalmerSeth WellsAmy LoganApril PetersonRobert LeonSherry BautistaAmy McdonaldAndrea SmithJames ZimmermanJessica GloverJulia BakerKenneth StuartKimberly SalinasLaura SmallMr. Joshua FranklinLisa GillAmber ElliottMichael KingLisa DavisTimothy JohnstonJulie NavarroMark NobleAmanda GuzmanEric Valdez MDJacqueline DavisJames McdonaldKevin WilkersonMatthew MorseStephanie VangDavid ColemanKelly KimTracy JimenezAaron BakerClifford ValenciaColin HensleyDiana SimmonsErika MooreGavin PrestonJeffrey WangKatie WilliamsKevin HerreraMatthew MelendezMrs. Amanda Williams MDNicole SheppardShannon RamseyTiffany BrownThomas SmithAndrea KingAndrew Williams MDBrittany LucasCaitlyn HancockColleen BrooksJennifer FlynnJimmy JonesJulia RiveraKarina MedinaKelly Roberts DVMMichelle DuarteNatalie MathisScott GarciaStephen RyanSteven SmithAmy WeeksCarla CrawfordDaisy PorterErik JohnsonEvan HawkinsHeather LucasJulia GibbsMark KennedyPatricia RowlandRyan RoblesSeth EstradaWalter CortezGwendolyn HermanJamie LongChristopher TurnerAndrew MartinezAngela SchultzBenjamin WadeCheryl ChangChristian NorrisCody DavilaDavid ParksDouglas Schwartz DDSEric ArnoldGeorge HansenJohn JohnsonJoseph MckenzieJoshua GilbertKelly PowellKimberly WebsterMarc PayneMegan MartinNathan GalvanRhonda SchmidtSarah WrightTammie ClarkThomas DanielsValerie Barron

---

Select by groupCompany ContactConsumer-goods CompanyMulti-role EntityShareholderTransport-logistics CompanyUltimate Beneficial Owner

5.3 Fishing-related and Transport-logistics Industries

Code

# Consumer Goods and Transport Industries
nodes_fishy3 <- nodes_industry %>%
  filter(group =="Fishing-related Company" | group =="Transport-logistics Company")

# Overall links
links_fishy3 <- mc3_links_new %>%
  filter(source %in% nodes_fishy3$id)

# Overall nodes from links data
links_fishy3_source <- links_fishy3 %>%
  distinct(source) %>%
  rename("id" = "source")

links_fishy3_target <- links_fishy3 %>%
  distinct(target) %>%
  rename("id" = "target")

# Overall Companies
nodes_fishy3_new <- bind_rows(links_fishy3_source, links_fishy3_target) %>%
  left_join(mc3_nodes_all, by = "id") %>%
  select(id, group, revenue_group, transboundary)

links_fishy3_new <- links_fishy3 %>%
  rename("from" = "source",
         "to" = "target")

Code

net7 <-
  visNetwork(
    nodes_fishy3_new, 
    links_fishy3_new,
    width = "100%",
    main = list(text = "Fishing-related and Transport-logistics Networks",
                style = "font-size:17x;
                weight:bold;
                text-align:right;")
  ) %>%
  visIgraphLayout(
    layout = "layout_with_fr"
  ) %>%
  visGroups(groupname = "Fishing-related Company",
            shape = "icon",
            icon = list(code = "f21a",
                        size = 45,
                        color = "#52DFD6")) %>%
  visGroups(groupname = "Transport-logistics Company",
            shape = "icon",
            icon = list(code = "f48b",
                        size = 45,
                        color = "#85929E")) %>%
  visGroups(groupname = "Ultimate Beneficial Owner",
            color = "#D86171") %>% 
  visGroups(groupname = "Shareholder",
            color = "#247EBF") %>% 
  visGroups(groupname = "Company Contact",
            color = "#F39C12") %>% 
  visGroups(groupname = "Multi-role Entity",
            color = "#48C9B0") %>% 
  visLegend() %>%
  visEdges() %>%
  addFontAwesome() %>%
  visOptions(
  # Specify additional Interactive Elements
    highlightNearest = list(enabled = T, degree = 2, hover = T),
  # Add drop-down menu to filter by company name
    nodesIdSelection = TRUE,
  # Add drop-down menu to filter by category
    selectedBy = "group",
    collapse = TRUE) %>%
  visInteraction(navigationButtons = TRUE)

net7

Fishing-related and Transport-logistics Networks

Select by id5 Limited Liability CompanyAdriatic Squid Ltd. Liability CoAdriatic Tuna Seabass BV TransitAncla del Este LCAqua Aura SE Marine lifeAqua Ventures Sp OverseasAquatic Adventures GmbH & Co. KGArunachal Pradesh s AS Marine sanctuaryArunachal Pradesh s Sagl TransportAsiotr Ltd. Liability Co TransitBahía Azul Aquamarine Corporation and Son'sBahía de Coral Ltd. Corporation TransportBahía de Plata ASBalkan Trout S.A. de C.V.Baltic Sprat Incorporated InvestmentBarco de Plata IncorporatedBarco de Plata SaglBarents Sea Shrimp LC MarineBarents Sea Shrimp OJSC _Belgian Shrimps Oyj BrothersBig Sulphur Company DeliveryBlue Water Ventures Sea GmbH ConsultingBrisa Marina CJSC BonitoYenisei Eel GmbH & Co. KG ServicesBu yu wang SpCape Verde Islands PelicanCape of Good Hope BVChobe River and Son'sCoastalCrave S.A. de C.V. SolutionsCoral Cove Marine mist LC ExportCoral Kingdom NV SolutionsCosta de Oro S.p.A.Costa del Sol S.p.A.Delhi Ltd. Liability CoDiao yu BV LogisticsDutch Bill A/S DistributionDutch Mussels S.A. de C.V. LineDutch Oyster Shipping LeskaElara Mussel AS Marine biologyEstrella de Mar LLC ShippingEstrella de Mar N.V.Estrella del Mar ОАО TransitEukrante GmbH & Co. KG LogisticsEukrante Marine biology A/S ExpressFin Flair ОАО CarriersFrench Sole AB LighthouseGreat Ruaha River OceanfrontGreek Octopus Limited Liability CompanyGreek ermen Kga FreightGroup and Son's Dry dockHaryana Sea Marine ecologyHaryana s A/S CargoHaryana s Catchers ОАО EnterprisesHaryana s Limited Liability Company SolutionsHenties Bay Ltd. Corporation FamilyIrish Sea Incorporated TransitIsla del Mar Ltd. Liability Co EnterprisesItalian Anchovy LC InternationalJammu & Kashmir AB ConsultantsJammu & Kashmir Sea Cod SRL CargoKambala Coral Co ShippingKong zhong diao yu Ges.m.b.H.Lake Chad Limited Liability Company ExportLake Malawi Corp UnitedLake Tanganyika Nori LLC BrothersLake Turkana Marine sanctuary NV and Son'sLake Victoria Catch GmbHPlaya de la Luna CJSCLimpopo River AB ConsultantsLine S.A. de C.V.Ltd. Corporation Ltd. CorporationLtd. Liability Co SRLMadhya Pradesh Market Marine mistManipur Market Sp ForwadingMar de Cristal Roe NV ShippingMar de Cristal S.A. de C.V.Mar de Plata YachtMar de la Aventura TidepoolMar del Paraíso ОАОMarina Oceanography Limited Liability Company DeliveryMarine Muse Pic Marine ecologyNagaland Sea Catch AB SolutionsNeptune's Harvest Ltd. CorporationNeptune's Realm Ltd. CorporationManipur Market Ltd. Liability CoNiger River Delta Inc MarineNiger River Delta S.p.A.OOO Ltd. Liability CoOcean Blue Ges.m.b.H. OverseasOcean Emporium LLC _Oceanfront Oases - Ltd. Liability Co Marine lifeOceanicOrigin Foods Cargo ship Sagl ShippingOla Azul Ltd. Liability CoOla del Mar PlcOlas del Mar LtdOlas del Mar Oyj EnterprisesOlas del Mar WorldwideOrange River Mouth BVPerla del Mar Corporation and Son'sPerla del Mar SashimiPlaya Azul Ltd CargoPlaya de Coral Hijiki OJSC TransitPlaya de Coral Oyj ImportPlaya de Oro Inc UnitedPlaya de Oro ОАО LockPlaya de la Felicidad Sagl TrawlerPlaya del Cielo KgaPlaya del Cielo Sextant LC FamilyPlaya del Mar N.V. SeafoodPlaya del Sol ASRajasthan Market Limited Liability CompanyReefRider Foods ОАО ShippingRichard GmbH & Co. KGSahara LLC LC MerchantsSaharan Coast Ges.m.b.H. ShippingSaltwater Sanctuary Worldwide LaksaSaltwater Sisters Ges.m.b.H. TankerSaltwater Sisters SRL CargoSaltwater Solitude OJSC TransportSaltwater Surf Club AB BrothersSamaka Chart ОАО DeliverySamaka Ltd. Liability Co DistributionScania Ltd. Liability CoScottish Trout Pic SolutionsSea Breezes Sagl SeasideSea Harvest CJSC Marine sanctuarySea Harvest Plc TransitSea Spray Starfish N.V. OverseasSea Striders Kelp BV SolutionsSeaBounty Tidepool AG MerchantsSeashore Sanctuary Marine lifeSeashore Sanctuary S.p.A. MarineSeashore Sanctuary Salt spraySeaside Summit Tidal Incorporated LineSpanish Anchovy GmbH & Co. KGTamil Nadu LC Marine lifeTamil Nadu Market Ltd. CorporationThe Sea Turtle GmbH & Co. KG FreightTiao piao Ltd. CorporationTransit Limited Liability CompanyTunaTreat Surf Inc ExpressTurkish Mussels Tsunami Ltd ServicesTurkish Sword N.V. LogisticsVictoria Falls Limited Liability CompanyViento Azul Incorporated ForwadingVolga River LC ServicesWater Wonders Limited Liability CompanyWest Bengal Ltd. CorporationWest _ GmbH & Co. KG TransitKallianassa Fish AG Solutionssuō yú Ltd. Liability Cojīng Coral LLC Carriersshí rén yú Marine lifexún yú AB Consultantsxún yú Inc LineBrittany JonesElizabeth TorresSandra RobertsAngelica WheelerShelly StrongMatthew MoraRyan StanleyTina WilsonVernon CoxElizabeth WhiteJennifer BauerRachel SanfordAlan TateEduardo NavarroJack SharpRichard GilbertSharon JeffersonStephanie ContrerasThomas ArmstrongEdward BryantDavid GoldenWilliam FrederickWayne GentryMrs. Crystal Miller MDHeather KnightAdam JohnsonAllison SampsonCharles HillChris MedinaDavid VillarrealDavid WilliamsonDeborah HuangJason GoodwinMrs. Kelly KimRichard YoungSharon HarringtonStephen BaileyElizabeth DillonSharon WallaceJose RamirezBeth HarveyBeverly BentonBrittany NelsonKayla JamesPaige BeckerSteven NobleLisa HouseMiss Jessica CollinsBailey BruceJoseph JohnsonMichael RichardsonJose MoyerRobert BennettChristopher EspinozaGregory WilliamsAndrew HouseCameron SantosDennis JosephDillon GuzmanFrederick ConwayJonathan FergusonJoshua AtkinsJustin DuncanLeslie HaleMatthew JohnsonMelody GlassNathan PetersonSeth ScottShawn TannerStefanie RoseTonya MullinsJodi GarciaKelly MclaughlinMrs. Stacie Morton DDSEdward PattonEric MartinezAntonio SnowDeborah GonzalezDaniel SanfordRonald CarterMark BurnsThomas HicksCurtis MunozGary HoffmanJustin HernandezMackenzie WarnerRobert GriffinTiffany MossZachary SheppardDr. Donald GeorgeRobert CarpenterHaley PhillipsMark ArellanoMichele RobinsonPhillip GarciaColton MyersDerek JonesChristina WilliamsRebecca KirkStephanie BrownHeather EllisLaura MaddoxHeather Chandler MDAdam StevensDeborah TaylorRicky ClarkAlexander SmithJeffrey TownsendRobert BallardJames ClarkJulie PorterDaniel RodriguezJason CardenasBenjamin GlassWilliam KimDouglas HendersonJennifer PattersonJeremy JacobsMelissa DavisRandy JohnsonAngela PerezAlison MoralesAmy PayneAshley RossBrett AndersonChristopher MillerColleen VangDavid NelsonDonna HernandezGeorge BurkeGrant HensonJeffery JonesJeffrey BuchananJeremy MathisJermaine MendezJonathan PittmanJustin AndersonJustin BrightKara GutierrezLarry DoughertyMarvin BoydMelanie BrownMelissa ThomasMichael FoleyMrs. Cynthia AvilaPatrick BirdRachel ReidRebecca CaldwellRobert CookSteven VasquezSusan MeadowsVincent SingletonChristine LindseyAnthony CastroChristopher EdwardsMary MillerCynthia MurphyDale RhodesJillian WhiteLeah CruzJustin LopezThomas DavidsonZachary MartinJessica MoorePeggy BuchananAmy KellyTracy MarshallAustin BaileyCalvin CoxJacqueline MyersJoseph SmithMichael Edwards DDSStephanie CrawfordJordan CruzAlyssa VaughnDavid HawkinsDawn ManningDonald ParkerEmily FrancoHeather WrightRhonda McdonaldMichael EwingKelly FreemanDouglas SimpsonJean PachecoJonathan GonzalezKaitlyn YangSabrina EdwardsGregory KennedyClaudia Watson DVMBarbara GarrettCarl StanleyChristian AshleyDonald AdamsJohn MonroeJohn WebbJustin FieldsKathryn WadeLance JohnsonMartin JohnsonMaurice JonesWilliam MitchellWilliam SandersAdam VargasAngel KingApril HooperAshley HolmesAustin DavisBrian WashingtonBrittany HernandezChristine IrwinConnie Gomez DDSDaniel DanielsDebra StephensonDonna HollowayHeather MccannHeather PatrickJack SantiagoJames ObrienJermaine BauerLance CampbellMichael BellMichael StoneRebecca JonesSally DodsonSamantha KennedySean HamiltonStephanie JohnsonTanya HartTaylor CunninghamTracey BellWesley ValenzuelaJoseph BirdVeronica ParkerDavid MendezRobin ClarkeStephanie PetersonJames SosaTimothy HarveyAlexandria SmithAmanda HaleAmanda MarshAnna DavisAshley HarrisBrent LucasBryce SmithChristopher AnthonyDr. Keith BishopDr. Sean RossEric FosterHailey MirandaJeffrey MendezJennifer HillJoseph SotoLisa LopezMark JacksonMatthew LopezMelissa RussellMichele CastilloMonica ChaseNicholas OchoaPhillip HallReginald FrederickRichard DeanSteven ByrdTina LoweryZachary MartinezSarah BuchananCrystal PriceHaley JensenHenry OliverMaria MartinPamela WhiteRobert IbarraSheena AlvaradoAngela BoydDenise CookJacob BrownJose WilliamsJessica MorrisDavid ThomasAmy WongAngela TaylorDale CantrellChristopher BrayGabriel FreemanSarah WilliamsJames MeyerLori NicholsonKelly HarrisonCole AllenShawn MyersCharles Luna IIDanielle AndersonMartin WhitakerSandra AdamsTanner JacksonCharlene ElliottTerry HartMatthew OlsenBeth HansonJeffrey BallTiffany WiseBreanna GravesEric BatesSteven HenryJessica MayDr. Charles RochaEmily BaileyAndrea WadeBrittney PhillipsKatrina StoneMadison EnglishMary McdonaldIan SwansonTimothy BakerLaura BryantRonald BrownBarbara GlennKathleen MeyerLaura MooreAlicia JordanJessica MclaughlinStephanie MasonMrs. Dana Reid DVMAlison ShafferJustin Robles DDSGeorge ChanRachel ButlerMichael DixonJohn NewmanAmanda MarquezStephanie QuinnTammy FieldsTimothy ParkerAlexandria MarquezMichael GonzalesDaniel ScottAmber PhillipsJames MedinaKathleen GordonMary MedinaDanielle LopezTammie WoodMaria HarrisAnita WaltonAnthony DavidJohn WalkerTammy KellerElizabeth ReidEmily HodgesRoger ChapmanAbigail RobinsonCynthia OrtizDr. Justin PrestonMatthew HarrisErika GilbertNathan MarquezAlexander MorrisSue RomeroLance HaynesRobert ThomasTony MasonDennis ShermanNatalie KennedyJames StevensonJennifer TaylorLouis OconnellScott PerezHannah LopezSara WrightBeverly MooreSean BaxterMelody SkinnerTonya PenaNatalie JensenJonathan WilliamsNicholas LangRicardo ThomasRobert PhillipsBrooke YoungCaleb PooleDominique BatesJennifer LambertKaren MillerMichael ReidMr. Steven HowardSamuel WilliamsWillie GardnerDana PalmerDanielle ColeSherry BautistaKimberly OrtizLisa GillAmber ElliottMichael KingTimothy JohnstonJulie NavarroHannah ReyesAllen GriffinChristopher JimenezGeorge BrownJohn DavisSean StewartKayla AdamsKelly KimJenna RiveraAlexandra SmithThomas SmithGwendolyn HermanJamie LongChristopher Turner

---

Select by groupCompany ContactFishing-related CompanyMulti-role EntityShareholderTransport-logistics Company

.
.

From the analysis thus far, we can surmise that anomalous company structures may be derived from inter-industry networks, understanding any underlying links between companies as well as looking for overlapping ownership structures. Separate graphlets (clusters) within the network that are linked by entities could suggest possible joint ventures – this is most apparent in the Industrial, Fishing and Seafood-processing network, where several pairs of companies (of different industries and sub-clusters) are linked by one or more entities. These companies are:

• SeaSolutions Leska Kga Worldwide (Seafood-processing) & Karelians Plc Marine conservation (Industrial), linked by John Williams (Multi-role Entity)

• Aqua Aura SE Marine life (Fishing-related) & Irish Mackeral S.A. de C.V. Marine Biology (Seafood-processing), linked by Dale Rhodes & Cynthia Murphy (Shareholders) and Leah Criz & Jilian White (Company contacts)

• Seashell Sanctum Co Family (Industrial) & Khundu N V and Son’s (Seafood-processing) linked by Amy Martinez (Shareholder)

• Adriatic Tuna Seabass BV Transit (Fishing-related) & Fisher Marine Conservation CJSC Transportation (Industrial), linked by Mathew Mora (Shareholder)

Of particular interest is the fact that in almost all of these flagged pairs of linked companies, one of the companies have a company name that suggests involvement in the study of marine biology or conservation efforts: Karelians Plc Marine conservation, Irish Mackeral S.A. de C.V. Marine Biology, etc. This is particularly fishy, and reminiscent of the case study on Japanese Whaling industry, where Japanese whalers hunt whales under the guise of ‘scientific research’, exploiting a loophole in International Whaling Commission (IWC) rules, which allows whaling for conservation research purposes. Similarly, these fishy structures could be hiding IUU activity under the guise of reasearch or conservation efforts.