To complete this assignment, please provide a detailed written summary of your analysis of the US Congress network dataset. The data are
data mining
Description
To
complete this assignment, please provide a detailed written summary of your
analysis of the US Congress network dataset. The data are
https://drive.google.com/file/d/1Lv_2HGpd070MmEoglTn2M_piS0T949jE/view?usp=sharing
Read
them using the read.graph() command with format=GraphML as the argument.
Specifically
you need to: (Links to an external
site.)
1. Use the dataset to
make a four social network maps using iGraph. The data are links
between House members who served on a committee together. Additional
information in the network file are from https://www.govtrack.us/congress/members/report-cards/2019 (Links to an external site.)
o VERTICES
o cosponsored: the
number of bills cosponsored by each legislator in 2019
o party: the political
party of the politician
o rank_from_high: the
politician's seniority rank among 437 total house members
o a,b,c,d: columns that
name the committee affiliations of a politician
o
o EDGES
o cosponsored: see above
o chamber: the House of
Senate; all values are for the House
o percentile: the
percentile rank associated with a politician's seniority in the House; see
rank_from_high above
o district: the geographic district (Links to an external
site.) the politician represents
o committee assignments:
see a, b, c, d above
o
2. NOTE: This is a very
large network. Your first steps might well be to subset this network and focus
on portions of it. Take advantage of the network attributes and network
statistics to subset it.
3. Calculate different
SNA statistics and use them as either node or edge attributes of the
network.
4. Use at least one
factor and one numeric variable as attributes. This means you should use
factors/numerics as ways to adjust the color, shape, size of nodes and edges.
You can create your own variables as appropriate.
5. Include two ggplot visualizations
of the network stats. Use ggrepel to label a subset of data
points.
6. Remember to use
techniques such as subsetting, side-by-side or faceted layouts.
7.
Include paths and cliques to analyze the data.
