Introduction

The purpose of this homework is to familiarize yourself with bipartite graphs and two-mode networks, projection, and weighted matrices in R. Please review the following materials before beginning this homework:


The Networks

For this homework assignment, there are two network objects to work with:

The first network is constructed from data recorded in Paul Revere’s Ride, by David Fischer. In the book, Fischer documents Reveres connections through various affiliations in locations and how these influenced history. The Paul Revere conspiracy dataset concerns relationships between 254 people and their affiliations with seven different organizations in Boston. The dataset refers to Paul Revere, who was responsible for organizing a local militia of Boston’s revolutionary movement. The network is two-mode, with 254 actors and 7 organizations (“events”). We will refer to this network as the PaulRevereNet.

The second network is the Philippine Kidnappings Data which is a collection of relationships on the Abu Sayyaf Group (ASG), a violent non-state actor operating in the Southern Philippines. In particular, this data is related to the Salast movement that has been founded by Aburajak Janjalani, a native terrorist of the Southern Philippines in 1991. ASG is active in kidnapping and other kinds of terrorist attacks. The object is a two-mode network, with 246 kidnappers and the 105 terrorist events they attended. We will refer to this as the PhilKidnapNet network.


Part I: Two-Mode Networks

For either (but if you are feeling it, do both!) the PaulRevereNet network or(and?) the PhilKidnapNet network, do the following:

  1. Plot the network using the gplot() function.
  2. Calculate the density of the network.
  3. Interpret the density of the network.
  4. Calculate the degree centrality scores for each set of nodes.
  5. Calculate the mean degree centrality scores and interpret the values.
  6. Calculate the standardized degree centrality scores for each set of nodes.
  7. Calculate the mean standardized degree centrality score.
  8. Plot the network using the standardized degree centrality scores.
  9. Calculate the dyadic clustering score.
  10. Interpret the dyadic clustering score.
  11. Using the vertex attribute corresponding to the names, discuss which node in each node set (i.e. N and M) is most central in the network.

Note: for 11, you can access the vertex attribute for the PaulRevereNet network using the PaulRevereNet %v% "people.names" and PaulRevereNet %v% "place.names", respectively. For the PhilKidnapNet network, the names of the nodes are stored as a vertex attribute called vertex.names.


Part II: Projection and Unipartite Networks

For either (but if you are feeling it, do both!) the PaulRevereNet network or(and?) the PhilKidnapNet network, do the following:

  1. Create an object of class matrix from the network. Use the as.sociomatrix() function in the network package to do so.
  2. Create the unipartite graph that represents the “person” matrix.
  3. Create the unipartite graph that represents the “group” matrix.
  4. Plot all three graphs.
  5. Create an object of class network from the dichotomized “person” matrix. Use this object for steps 6-11.
  6. Calculate the degree, closeness, and betweenness centrality scores for each actor.
  7. Calculate the standardized degree, closeness, and betweenness centrality scores for each actor.
  8. Calculate and report the mean (unstandardized) degree, closeness, and betweenness centrality scores.
  9. Interpret each mean centrality score.
  10. Calculate the graph centralization for degree, closeness, and betweenness centrality.
  11. Interpret each graph centralization score.


How to Submit

Download the Homework 4 template prior to beginning. The template contains code for accessing the data files.


Knitting to HTML

When you have completed your homework, click the “Knit” button to render your .RMD file into a .HTML report.


Special Instructions

Upload both your .RMD and .HTML files to the appropriate assignment on the Canvas page for this course. Just click the “Start Assignment” button to upload the files. This assignment is DUE on 3/13.


Before You Submit

Remember to ensure the following before submitting your assignment.

  1. Name your files using this format: Hmwk-##-LastName.rmd and Hmwk-##-LastName.html
  2. Show both the solution for your code and write out your answers in the body text

See Google’s R Style Guide for examples of common conventions.


Common Knitting Issues

.RMD files are knit into .HTML and other formats procedural, or line-by-line.

  • An error in code when knitting will halt the process; error messages will tell you the specific line with the error
  • Certain functions like install.packages() or setwd() are bound to cause errors in knitting
  • Altering a dataset or variable in one chunk will affect their use in all later chunks
  • If an object is “not found”, make sure it was created or loaded with library() in a previous chunk

If All Else Fails: If you cannot determine and fix the errors in a code chunk that’s preventing you from knitting your document, add eval = FALSE inside the brackets of {r} at the beginning of a chunk to ensure that R does not attempt to evaluate it, that is: {r eval = FALSE}. This will prevent an erroneous chunk of code from halting the knitting process.



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