In Ecological-Complexity-Lab/infomap_ecology_package: Community Detection using Infomap, Inspired by Ecological Networks

# Use this to add a copy-code buttong
klippy::klippy(position = c('top', 'right'), color = 'darkred')

knitr::opts_chunk$set(echo = TRUE)

Data set

A binary directed food web from Mouritsen KN, Poulin R, McLaughlin JP, Thieltges DW. Food web including metazoan parasites for an intertidal ecosystem in New Zealand: Ecological Archives E092-173. Ecology. 2011;92: 2006–2006.

In infomapecology:

data(otago_nodes)
data(otago_links)

Input

1. A link-list.
2. A second file that indicates the ID of the attribute for each node ID:

node_id attribute_id
1 1
2 1
3 1
4 2
5 3

In this example, the attribute IDs are (example with first four):

OrganismalGroup	attribute_id
Plant	1
Zooplankton	2
Meiofauna	3
Bacteria	4

For now, Infomap can only handle categorical attributes, and only a single attribute type.

R Code

The code here depends on previous analysis of the Otago network with hierarchical structure, which should be run first.

# Prepare data for hierarchical structure analysis -------------------------------
otago_nodes_2 <- otago_nodes %>%
  filter(StageID==1) %>%
  select(node_name=WorkingName, node_id_original=NodeID, WorkingName,StageID, everything())
otago_links_2 <- otago_links %>%
  filter(LinkType=='Predation') %>% # Only include predation links
  filter(ConsumerSpecies.StageID==1) %>%
  filter(ResourceSpecies.StageID==1) %>%
  select(from=ResourceNodeID, to=ConsumerNodeID) %>%
  left_join(otago_nodes_2, by=c('from'='node_id_original')) %>%
  select(from, node_name, to) %>%
  left_join(otago_nodes_2, by=c('to'='node_id_original')) %>%
  select(from=node_name.x, to=node_name.y) %>%
  mutate(weight=1)

# Prepare network objects
# Some species will have only incoming or outgoing links, so the next line will result in a warning
network_object <- create_monolayer_network(x=otago_links_2, directed = T, bipartite = F, node_metadata = otago_nodes_2)


# Prepare data for analysis with node attributes ---------------------------------
# Create an attribute -- attribute ID map
node_attribute_map <- otago_nodes_2 %>% distinct(OrganismalGroup) %>%
  mutate(attribute_id=1:n())

# Create a file with node attributes
node_attributes <-
  network_object$nodes %>%
  distinct(node_id, OrganismalGroup) %>%
  left_join(node_attribute_map) %>%
  select(-OrganismalGroup) %>%
  write_delim('otago_node_attributes.txt', delim = ' ', col_names = F)

# Run without metadata
infomap_object <- run_infomap_monolayer(network_object, infomap_executable='Infomap',
                                        flow_model = 'directed',
                                        silent=T,trials=5, two_level=T, seed=200952)

# Run with metadata and eta=0.7
infomap_object_metadata_09 <- run_infomap_monolayer(network_object, infomap_executable='Infomap',
                                                 flow_model = 'directed',
                                                 silent=T,trials=5, two_level=T, seed=200952,
                                                 ... = '--meta-data otago_node_attributes.txt --meta-data-rate 0.9')
# Run with metadata and eta=1.3
infomap_object_metadata_13 <- run_infomap_monolayer(network_object, infomap_executable='Infomap',
                                                 flow_model = 'directed',
                                                 silent=T,trials=5, two_level=T, seed=200952,
                                                 ... = '--meta-data otago_node_attributes.txt --meta-data-rate 1.3')


# Compare the modules with and without metadata
eta0 <- infomap_object$modules %>%
  group_by(module_level1) %>%
  summarise(n=n_distinct(OrganismalGroup)) %>%
  mutate(eta=0) %>%
  arrange(desc(n), module_level1)
eta09 <- infomap_object_metadata_09$modules %>%
  group_by(module_level1) %>%
  summarise(n=n_distinct(OrganismalGroup)) %>%
  mutate(eta=0.9) %>%
  arrange(desc(n), module_level1)
eta13 <- infomap_object_metadata_13$modules %>%
  group_by(module_level1) %>%
  summarise(n=n_distinct(OrganismalGroup)) %>%
  mutate(eta=1.3) %>%
  arrange(desc(n), module_level1)

bind_rows(eta0, eta09, eta13) %>% 
  mutate(eta=factor(eta, levels=c('0','0.9','1.3'))) %>% 
  rename(num_OG=n, module_id=module_level1) %>% 
  group_by(num_OG, eta) %>%
  summarise(num_modules=n()) %>%
  ggplot(aes(x=num_OG, y=num_modules,fill=eta)) +
  geom_col(position = 'dodge', color='black', width=0.7) +
  scale_x_continuous(limits=c(0,18), breaks=0:18)+
  scale_y_continuous(limits=c(0,37), breaks=seq(0,36,by=3))+
  scale_fill_manual(values = c('#fc8d62','#8da0cb','#e78ac3'))+
  labs(x='Number of organismal groups', y='Number of modules', fill='eta')+
  theme_bw()+
  theme(panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        axis.line = element_line(color = 'black'),
        legend.position = c(0.8,0.8),
        legend.text = element_text(size=16),
        legend.title = element_text(size=16),
        legend.key.size = unit(1, 'line'),
        axis.text = element_text(size=16),
        text=element_text(size=16))

Infomap

Under the hood, the function run_infomap_monolayer runs:

./Infomap infomap.txt . -i link-list --tree --seed 200952 -N 20 -f directed --silent --two-level --meta-data otago_node_attributes.txt --meta-data-rate 0.7

Explanation of key arguments: --meta-data otago_node_attributes.txt indicates the file name (otago_node_attributes.txt) with the attribute value for each node. --meta-data-rate 0.9 indicates the value of eta (here 0.9)

Output

A tree file is produced by Infomap, but is parsed by run_infomap_monolayer from infomapecology (in R: ?run_infomap_monolayer). An analysis with metadata does not have a special output. So in this example the output format is the same as in Monolayer directed network with hierarchical structure

Ecological-Complexity-Lab/infomap_ecology_package documentation built on June 6, 2024, 5:28 a.m.