Navigate
Home Compendium
Exercises
Tidyverse packages
dplyr ggplot2 tidyr stringr lubridate purrr forcats readr broom Tidyverse R for Data Science
Deep dives
dplyr Joins dplyr Window Functions dplyr group_by ggplot2 Themes ggplot2 Facets ggplot2 Color Scales ggplot2 Bar Charts ggplot2 Heatmaps tidyr Pivot tidyr Nest/Unnest Regex
Wrangling & EDA
Data Wrangling Data Cleaning EDA Data Visualization data.table dbplyr / SQL Web Scraping API Calls
Statistics
Hypothesis Testing Linear Regression Logistic Regression Correlation Probability Distributions
Machine Learning
Machine Learning tidymodels Random Forest XGBoost Clustering PCA Cross-Validation
Time Series
Time Series ARIMA
By Industry
Finance Marketing Analytics Healthcare Biostatistics Genomics Sports Analytics A/B Testing
Reporting & Apps
Shiny R Markdown
Levels
R for Beginners R Interview Questions
R r‑statistics.co

Network Analysis Exercises in R: 15 Practice Problems

Fifteen practice problems on network analysis in R with igraph: degrees, centrality, communities, paths, viz.

By Selva Prabhakaran  ·  Published May 11, 2026  ·  Last updated May 11, 2026
RRun this once before any exercise
library(igraph)

  

  





Exercise 1: Create graph from edges



Difficulty: Beginner.



Show solution



  

    
RInteractive R

    
graph_from_literal(A-B, B-C, C-D, A-D)

    

      
      
    

    

  

  








Exercise 2: Number of nodes/edges



Difficulty: Beginner.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D)
list(vcount = vcount(g), ecount = ecount(g))

    

      
      
    

    

  

  








Exercise 3: Degree



Difficulty: Beginner.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D)
degree(g)

    

      
      
    

    

  

  








Exercise 4: Adjacency matrix



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D)
as_adjacency_matrix(g)

    

      
      
    

    

  

  








Exercise 5: Plot



Difficulty: Beginner.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D)
plot(g)

    

      
      
    

    

  

  








Exercise 6: Directed graph



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-+B, B-+C)
plot(g)

    

      
      
    

    

  

  








Exercise 7: Betweenness centrality



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D, B-D)
betweenness(g)

    

      
      
    

    

  

  








Exercise 8: Closeness



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D)
closeness(g)

    

      
      
    

    

  

  








Exercise 9: Eigenvector centrality



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D)
eigen_centrality(g)$vector

    

      
      
    

    

  

  








Exercise 10: Shortest path



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D)
shortest_paths(g, from = "A", to = "C")

    

      
      
    

    

  

  








Exercise 11: Connected components



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, C-D, E-F)
components(g)

    

      
      
    

    

  

  








Exercise 12: Community detection (Louvain)



Difficulty: Advanced.



Show solution



  

    
RInteractive R

    
g <- erdos.renyi.game(20, 0.2)
cluster_louvain(g)

    

      
      
    

    

  

  








Exercise 13: Edge list to data frame



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D)
as_data_frame(g)

    

      
      
    

    

  

  








Exercise 14: Read from data frame



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
edges <- data.frame(from = c("A","B","C"), to = c("B","C","D"))
graph_from_data_frame(edges)

    

      
      
    

    

  

  








Exercise 15: Density



Difficulty: Intermediate.



Show solution



  

    
RInteractive R

    
g <- graph_from_literal(A-B, B-C, C-D, A-D, B-D)
edge_density(g)

    

      
      
    

    

  

  








What to do next



    


  • Spatial-Analysis-Exercises (shipped), geographic networks.
    • 


  • Data-Visualization-Exercises (shipped), viz network plots.
    • 






          
Ready to earn the Network Analysis Certificate?
The quiz is concept-based and respects your time: pass it once and your verifiable certificate is yours to share on LinkedIn, your resume, or your portfolio. Take it when you feel comfortable with the material.
Attempt the quiz→


        


        
      


      

        

        
© 2016-2026 Selva Prabhakaran.
          This work is licensed under the Creative Commons License.
        

        

          About ·
          RSS