Introduction to Network Analysis Using R

Yunran Chen

2019-04-29

Chapter 1 Introduction

Here I provide a tutorial on basic network analysis using R. This tutorial is suitable for people who are familiar with R.

• The documents are based on the lab materials of STAT650 Social Network at Duke University.

• The webpage is built using bookdown

1.1 Outline

• Basic introduction on network objects. R packages including igraph, statnet(including sna, network).
• Collect network data.
  • Web API requesting (Twitter, Reddit, IMDB, or more).
  • Useful websites (SNAP, or more)
• Visualization (static and dynamic networks).
• Network analysis using package amen.

1.2 Detailed Outline

• igraph package
  • Create networks and basics concepts
    • Create simple networks, specific graphs, graph models
    • Adjustments on graphs, rewiring graphs
    • Edge, vertex and network attributes
  • Built networks from external sources, basic visualization and network descriptions
    • Get network from files (edgelist, matrix, dataframe)
    • Visualization
      • Plotting parameters
      • Layouts
    • Network and node descriptions
  • Paths, communitites and related visualization
    • Paths
      • Paths, distances and diameter
      • Components
      • Transitivity and reciprocity
      • Max-flow and min-cut
    • Communities
      • Pre-defined clusters
      • Different algorithms
    • Visualization
      • Color the paths
      • Plotting clusters
      • Plotting dendrograms
      • Mark groups
        
  • References:
    • Tutorial: http://kateto.net/networks-r-igraph
    • Handbook: http://igraph.org/r/
    • Book: https://sites.fas.harvard.edu/~airoldi/pub/books/BookDraft-CsardiNepuszAiroldi2016.pdf
    • Datasets: https://github.com/mathbeveridge/asoiaf https://networkofthrones.wordpress.com/data/ https://www.macalester.edu/~abeverid/thrones.html
• ERGM (statnet)
  • summary network statistics
  • ergm model fitting and interpretation:
  • simulate network simulations based on specified model.
  • gof, mcmc.diagnostics: Goodness of fit and MCMC diagnostics
  • References:
    • Tutorial: https://statnet.org/trac/raw-attachment/wiki/Sunbelt2016/ergm_tutorial.html
    • Handbook: http://statnet.org/
• Collect network data and API requests
  • Several useful websites:
    • Awesome website for papers, reviews, datasets and softwares: https://github.com/briatte/awesome-network-analysis
    • Katherine’s webpage: http://kateto.net/2016/05/network-datasets/
    • Data analysis competition: https://www.kaggle.com/datasets https://data.world/search?q=network+dataset
  • twitter API request: rtweet
    • Official website: https://rtweet.info/index.html
    • API authorization; create_token
    • search_tweets
    • stream_tweets
    • Other functions
    • Examples: retweet networks, friendship networks
    • Other available R packages for API request: rtweet,Rfacebook,RedditExtractoR,imdbapi,omdbapi
  • Direct API request from R. rjson, jsonlite
  • References:
    • https://rtweet.info/index.html
• Advanced Visualization
  • Visualization for static network:
    • Graph: hairball plot
    • Matrix: heatmap in R basic package; geom_tile in pkg ggplot2
  • Other static networks:
    • Two-mode networks (node-specific attribute)
    • Multiple networks (edge-specific attribute)
    • … ( ggtree, ggalluvial, etc.)
  • ggplot2 version for network visualization:
    • Comparison between ggnet2,geomnet,ggnetwork
    • Extension to interactive (plotly) , dynamic network (ggnetwork)
  • Other interactive network visualizations:
    • visNetwork (good documentation)
    • networkD3
    • threejs
    • ggigraph
  • Visualization for dynamic networks
    • Snapshots for the evolving networks: ggnetwork (common)
    • Animation for the evolving networks: ggplot2 + gganimate
    • ndtv pkg (good documentation)
  • References:
    • http://kateto.net/netscix2016
    • http://kateto.net/network-visualization
    • http://curleylab.psych.columbia.edu/netviz/
    • https://journal.r-project.org/archive/2017/RJ-2017-023/RJ-2017-023.pdf
• amen packages
  • Gaussian AME model: ame
  • Different relation: ame(...,model=,...)
    • ordinal data
    • censored and fixed rank nomination data
    • sampled or missing data
  • symmetric relation: ame(...,symmetric=TRUE,...)
  • repeated measures data: longitudal data ame_rep(Y,Xdyad,Xrow,Xcol)
  • References:
    • https://github.com/pdhoff/amen/blob/master/inst/doc/amen.pdf