xiyanghu/STA650Lab

Lab materials of STA650/450 Social Network.

STA650Lab

Website: https://yunranchen.github.io/intro-net-r/

Lab materials of STA650/450 Social Network will be posted here.

Lab session: Thursday 4:40 - 5:55 pm, Gray 228.

Office hour: Wednesday 12:00 - 1:00 pm, Old Chem 025.

Homework submission: via slack.

Contact me: via slack.

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.

If you are not familiar with R, see http://www2.stat.duke.edu/~cr173/Sta523_Fa16/

Detailed outline

• Lab 1: igraph basics

  • Create simple networks, Specific graphs, Graph models
  • Adjustments on graphs, rewiring graphs
  • Edge, vertex and network attributes
  • Network and node descriptions
  • 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

• Lab 2: igraph basics

  • Get network from files (edgelist, matrix, dataframe)
  • Visualization
    • Plotting parameters
    • Layouts
  • Network and node descriptions
  • 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

• Lab 3: igraph basics

  • 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

• Lab 4: Algorithm

  • Blitzstein and Diaconis sampling algorithm
    • Reference: https://projecteuclid.org/download/pdf_1/euclid.im/1318514519
  • Using Git in Rstudio

• Lab 5: 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/

• Lab 6: 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 see official website
    • Examples: retweet networks, friendship networks

• Lab 7: API requests

  • Build Twitter friendship networks
  • Other available R packages for API request: rtweet,Rfacebook,RedditExtractoR,imdbapi,omdbapi
  • Direct API request from R. rjson, jsonlite

• No lab on March 7th (Spring Break).

• Lab 8: Epidemiological modeling of news spreading on Twitter (course project)

  • References:
    • https://github.com/YunranChen/HiggsBoson

• Lab 9: 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

• Lab 10: AMEN packages (March 28th)

  • 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)
  • Tutorial: https://github.com/pdhoff/amen/blob/master/inst/doc/amen.pdf

• No lab session after March 28th.