Networks in the House

1. Networks in the House Porter, M.A., Mucha P.J., Newman, M.E., and Warmbrand, C. A Network Analysis of Committees in the United States House of Representatives. PNAS USA 102, 7057-7062 (2005). http://www-personal.umich.edu/~mejn/papers/congress.pdf

2. Overview • What: • Network Analysis of the US House of Reps • Committee Co-membership, Committee Interlock • Voting Records -> Party Affiliation • Why do you care? • Fun political interpretations • Interesting use of PCA to find groups • Data gathering techniques • Because it’s the network analysis status quo?

3. Overview • Interlock Structure Data • Clustering Results(groups of committees) • Voting Data • PCA Results (partisanship of issues and individuals) • Conclusions and Claims • Discussion

4. Structure: Normalized Interlock • Committee Interlock Network • Normalized by Signficance using QAP • Repeatedly assign members to committees randomly, and average number of connections to find expected interlock • Normalized Interlock = Observed links / Expected

5. Normalized Committee Interlock 107th House of Reps. (’01-’02) (Porter et al., 2004) Layout using spring embedding.

6. Single Linkage Clustering Pairs of committees joined sequentially, starting with highest normalized interlock. Layers of Organization: • Subcommittees • Committees : • Groups of Committees • Entire House

7. Single Linkage Clustering 107th House of Reps. (’01-’02) (Porter et al., 2004)

8. Overview • Interlock Structure Data • Clustering Results (groups of committees) • Voting Data • PCA Results(partisanship of issues and individuals) • Conclusions and Claims • Discussion

9. SVD Analysis (aka PCA) Reduce dimensionality of data by finding a few orthogonal vectors that can be combined to approximate each point. X + Y + Z

10. SVD Continued Also arranges axes to put as much information in early coordinates as possible. • Find Eigenvectors and eigenvalues of data matrix • First Eigenvector explains most variation in the matrix, and so-on…

11. Voting Matrix Votes Spector Frist Representatives DeLay Pelosi

12. Applying SVD • First Eigenvector • Correlates with Declared Political party • Explains 45.6% of voting matrix • Second Eigenvector • Correlates with probability of voting with majority of party (“partisanship”) • Explains 29.6% of voting matrix • The Rest • Accounts for 1.6% or less • Excellent approximation of votes with first 2

13. Partisanship and Bipartisanship of Representatives (Porter et al., 2004) O = Republican X = Democrat

14. Partisanship and Bipartisanship of Votes (Porter et al., 2004) O = Pass X = Fail

15. Single Linkage Clustering 107th House of Reps. (’01-’02) (Porter et al., 2004)

16. Overview • Interlock Structure Data • Clustering Results (groups of committees) • Voting Data • PCA Results (partisanship of issues and individuals) • Conclusions and Claims • Discussion

17. Conclusions / Claims • Methods • Presented normalization method for interlock graphs • Used SVD to detect underlying variables explaining votes

18. Conclusions / Claims • Results • Found levels of hierarchy in committee structure • Found correlation between committee assignments and partisanship • Found “non-subjective” measurements for both affiliation and partisanship