Networkanalysis for Behavioral ecology

# Install and load required packages
if (!require(igraph)) install.packages("igraph")
library(igraph)

# Generate a random network for demonstration
set.seed(123)
nodes <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
edges <- data.frame(from = sample(nodes, 15, replace = TRUE),
                    to = sample(nodes, 15, replace = TRUE))
graph <- graph_from_data_frame(edges)

# Plot the network
plot(graph, main = "Behavioral Ecology Network", vertex.label.dist = 2, vertex.color = "lightblue", edge.color = "gray")

# 1. Node-level metrics
# Degree centrality
degree <- degree(graph)
cat("Degree Centrality:\n")
print(degree)

# 2. Network-level metrics
# Density
density_value <- graph.density(graph)
cat("\nNetwork Density:\n")
print(density_value)

# 3. Centrality measures
# Betweenness centrality
betweenness <- betweenness(graph)
cat("\nBetweenness Centrality:\n")
print(betweenness)

# 4. Modularity
modularity_value <- cluster_optimal(graph)
cat("\nModularity:\n")
print(modularity_value)

# 5. Community detection
communities <- walktrap.community(graph)
cat("\nCommunities:\n")
print(communities$membership)

# 6. Eccentricity
eccentricity <- eccentricity(graph)
cat("\nEccentricity:\n")
print(eccentricity)

# 7. Average path length
avg_path_length <- average.path.length(graph)
cat("\nAverage Path Length:\n")
print(avg_path_length)

# 8. Network visualization
# Highlight communities in the plot
plot(graph, main = "Behavioral Ecology Network with Communities", vertex.label.dist = 2, vertex.color = communities$membership, edge.color = "gray")

# 9. Connectivity
is_connected <- is.connected(graph)
cat("\nIs Connected:\n")
print(is_connected)

# 10. Diameter
diameter_value <- diameter(graph)
cat("\nDiameter:\n")
print(diameter_value)