Visualizing Positive and Negative Endorsements of S.1782 (2007)


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Journal of Social Structure

International Network for Social Network Analysis

Subject: Social Sciences


eISSN: 1529-1227





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VOLUME 11 , ISSUE 1 (December 2010) > List of articles

Visualizing Positive and Negative Endorsements of S.1782 (2007)

Skye Bender-de Moll *

Citation Information : Journal of Social Structure. Volume 11, Issue 1, Pages 1-5, DOI:

License : (CC-BY-NC-4.0)

Published Online: 10-January-2020



Graphical ABSTRACT

A bi-partitie network of legislation and organizations



This image depicts a network of bills (squares) and endorsing organizations (circles) around S.1782 during the 110th U.S. Congress. The green and red ties indicate support or opposition of S.1782 by an organization. Gray ties link to additional bills positively endorsed by organizations. The information was collected by from various public documents. Color indicates similar group categorization, and the size of the nodes is relative to the total number of endorsements it gave/received in the database. Mousing over small nodes will reveal the title or additional bill info. Clicking on bills will load an associated web page.

S.1782 was chosen as the focus for this extended ego-network visualization because the title and description of the bill give very little indication about its intent or possible effects. MapLight’s table of supporters and opponents is quite helpful, but ideally it would be possible to simultaneously see where each of a bill’s endorsing organizations stands on other issues in order to place their endorsement in context. This layout results in intersecting circles of legislative preference around groups with similar patterns of endorsements, revealing separation and overlap between the camps surrounding S.1782. Opposition for the bill seems to have come from large industry lobby groups, corporations, and business associations. Support came from consumer and activists groups. The “nays” seem to have won, as the bill died in committee.

The layout was produced using SoNIA and the MDSJ library. The MDS algorithm was run 150 iterations on the matrix of all-pairs-shortest-path distances with a scaling exponent of -7 to weight distant ties less. Some node positions were manually tweaked for legibility.

Self comentary

Note: for this example to function correctly in addition to the png image it most load a script file, an xml data file, and include javascript in the header of the page. Not sure how this will actually work with the JOSS journal formated web page.

Data on the entire set of bill endorsements were kindly provided by in csv from. I loaded it into a MySQL database so that I could experiment with various types of networks. Although, the co-endorsing, and co-endorsee networks of the legislative space were in some ways more interesting, they would require much more work to make a presentable image. I also wanted to test the feasibility of using visualization to learn something about an arbitrarily chosen bill. I wrote a utility program in java to facilitate the process of testing various queries to select the node attribute data and construct the tie relations. The queries are processed to produce a .son formatted network file, which was loaded into SoNIA for visualization.

I initially expected that using different tie weights for the S.1782 ties and the rest of the bills ties would help structure the network. The approach was not successful, so I ended up simply giving the direct ties greater width to increase the visual impact, and graying out the ties to the other bills to focus attention on the bill of interest. Earlier versions of the image were produced with a KK layout, but I found the MDSJ MDS layout was somewhat more stable, and allowed me to adjust the distance parameter to control the “clumpyness” of the (loosely) structurally equivalent node groups. I adjusted some node positions and shortened some labels to reduce clutter in the resulting layout. A challenge was making the labels legible without crowding the layout or distracting from the ties. In the end, I settled for making many of the labels too small to read, but including a mouseover option to show the labels as a “tooltip” on the image in a web browser.

To prepare for the web version, I exported the .PNG image, and an .XML file containing node coordinates and labels from SoNIA. I adapted some JavaScript code previously written by a co-developer to read the xml file and produce the image mouseovers. I added a feature to parse the titles of the bills into an appropriate url on the GovTrack site, making it possible to click through to more bill information. I also inserted bill titles for selected nodes directly into the tooltip, hopefully making it possibly to quickly get a feel for the type of bills in each area without cluttering the layout with the long labels. This is important, because the bill numbers themselves are not meaningful and I do not have (or know of) any bill classification data that could be used to help the viewer determine if the bill groupings produced by the layout make sense, or what is implied about an organizations political position by an endorsement.

Although I think this is an interesting image, I see several issues. One is that the nodes that only endorse a single bill do not have well defined positions in this layout, they tend to land in arbitrary regions and their positions are likely to be falsely assigned significance by the viewer. As with most network visualizations, the groupings might be more rigorously created with a clustering algorithm. There may also be data coverage and sampling issues in the underlying data.


I really appreciate the author’s use of a careful color scheme of support and opposition so the viewer may easily discern coalition blocks. The visualization also includes other bills supported by each organization, which hints at a more contextualized story – would love to see this fully searchable/interactive. It appears that subgroups of organizations tend to support similar bills, but organizational coalitions are far from uniform; I’m guessing that sets of organizations may come together or fall apart depending on the particular bill of interest. Thus, while the visualization nicely captures the coalition structure for the particular bill of interest, there is, unfortunately, very little sense of how the other bills are interrelated.


The positioning of organizations that support and opposed the bill around Bill S 1782 made the two sides visually easy to locate. In addition to the corresponding edge color scheme, the image conveys a clear picture of the bill’s supporters as mainly activist groups and its opposition as mainly business organizations. While the color the organization is assigned shows their voting pattern, the reader may be overwhelmed with the volume of bills connected by grey lines and surrounding the organizations, would be nice to have a way to summarize the “similar” bills.


This visualization makes great use of user input, providing unabbreviated node identities during mouse-over, and detailed information when clicked. This provides a great circumvention of the trade-off between node information and graph cluster. It also employs an effective yet simple color/layout scheme to display a bipartite graph without the artificial separations that often stilt bipartite graphs. I wonder how this graph would look with the other edges colored (perhaps in faint red/green) according to whether the organization opposed S 1782 – would it give a sense of how firm these organizational battle lines hold across bills?

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