r-network

name	r-network
description	R network analysis packages. Use for graph analysis, social network analysis, network visualization, and community detection.

R Network Analysis Skill

Sub-skills

Sub-skill	Description
r-network-analysis	igraph, tidygraph, centrality, communities
r-network-viz	ggraph, visNetwork, networkD3
r-network-dynamic	networkDynamic, ndtv, temporal networks

Network and graph analysis in R.

Core Packages

Package	Description
igraph ★	Comprehensive network analysis
tidygraph ★	Tidy API for graphs
network	Basic relational data tools
sna	Social network analysis

Network Modeling

Package	Description
ergm	Exponential random graph models
latentnet	Latent position/cluster models
manynet	Many network types

Dynamic Networks

Package	Description
networkDynamic	Dynamic/temporal networks
ndtv	Animated network visualization
netdiffuseR	Network diffusion analysis

Visualization

Package	Description
ggraph ★	Grammar of graphics for graphs
visNetwork ★	Interactive visualization (vis.js)
networkD3	D3 network graphs
autograph	Automagic network plotting

Metrics & Analysis

Package	Description
tnet	Weighted/two-mode networks
rgexf	Export to GEXF (Gephi)

Quick Examples

# igraph basics
library(igraph)

# Create graph
g <- graph_from_data_frame(edges, directed = TRUE, vertices = nodes)
# Or from adjacency matrix
g <- graph_from_adjacency_matrix(adj_matrix)

# Basic metrics
vcount(g)  # Number of vertices
ecount(g)  # Number of edges
degree(g)  # Degree centrality
betweenness(g)  # Betweenness centrality
closeness(g)  # Closeness centrality
page_rank(g)$vector  # PageRank

# Community detection
communities <- cluster_louvain(g)
membership(communities)
modularity(communities)

# Shortest paths
shortest_paths(g, from = "A", to = "B")
distances(g)

# Plot
plot(g,
     vertex.size = degree(g) * 2,
     vertex.color = membership(communities),
     edge.arrow.size = 0.5)

# tidygraph + ggraph
library(tidygraph)
library(ggraph)

tg <- as_tbl_graph(g) %>%
  activate(nodes) %>%
  mutate(
    centrality = centrality_degree(),
    community = group_louvain()
  )

ggraph(tg, layout = "fr") +
  geom_edge_link(alpha = 0.5) +
  geom_node_point(aes(size = centrality, color = factor(community))) +
  geom_node_text(aes(label = name), repel = TRUE) +
  theme_graph()

# Interactive visualization
library(visNetwork)
visNetwork(nodes, edges) %>%
  visOptions(highlightNearest = TRUE) %>%
  visLayout(randomSeed = 123)

# Network statistics
transitivity(g)  # Clustering coefficient
diameter(g)  # Network diameter
graph.density(g)  # Density
assortativity_degree(g)  # Degree assortativity

Common Workflows

Social Network Analysis

library(igraph)

# Load data
edges <- read.csv("edges.csv")
nodes <- read.csv("nodes.csv")
g <- graph_from_data_frame(edges, vertices = nodes)

# Centrality analysis
nodes$degree <- degree(g)
nodes$betweenness <- betweenness(g)
nodes$eigenvector <- eigen_centrality(g)$vector

# Community detection
comm <- cluster_louvain(g)
nodes$community <- membership(comm)

# Key players
head(nodes[order(-nodes$betweenness), ])

Network Visualization

library(ggraph)
library(tidygraph)

tg <- as_tbl_graph(g) %>%
  activate(nodes) %>%
  mutate(importance = centrality_pagerank())

# Force-directed layout
ggraph(tg, layout = "fr") +
  geom_edge_link(aes(alpha = weight)) +
  geom_node_point(aes(size = importance)) +
  theme_void()

# Circular layout by community
ggraph(tg, layout = "linear", circular = TRUE) +
  geom_edge_arc(aes(alpha = weight)) +
  geom_node_point(aes(color = factor(community))) +
  coord_fixed()

Resources

• igraph: https://igraph.org/r/
• tidygraph: https://tidygraph.data-imaginist.com/
• ggraph: https://ggraph.data-imaginist.com/
• visNetwork: https://datastorm-open.github.io/visNetwork/