Encyclopedia of Social Network Analysis and Mining

Editors: Reda Alhajj, Jon Rokne

Legislative Prediction with Political and Social Network Analysis

  • Jun Wang
  • Kush R. Varshney
  • Aleksandra Mojsilović
DOI: https://doi.org/10.1007/978-1-4614-6170-8_285

Synonyms

Glossary

IPM

Ideal point model

IPTM

Ideal point topic model

Heterogeneous Graph

Refers to a graph with multiple types of nodes and edges

RWHG

Random walk over a heterogeneous graph

Political Affinity

Refers to the connections such as cosponsorship relations between legislators

Definition

The function of legislatures is to propose and vote on new laws. In some systems of government, including parliamentary governments that follow the Westminster system, the party affiliation of legislators is codified in the constitution, and legislators are bound to vote in lockstep with their party. However, in other systems of government, party affiliation is only one of many factors that influences a legislator's voting yea or nay. Ideology and political and social relationships are key components in a legislator's voting decision.

A bill is a proposed law under consideration...

This is a preview of subscription access content, login to check access

References

  1. Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on web search and data mining, Hong Kong, pp 635–644Google Scholar
  2. Banerjee O, El Ghaoui L, d'Aspremont A (2008) Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. J Mach Learn Res 9:485–516MATHMathSciNetGoogle Scholar
  3. Beck PA, Dalton RJ, Greene S, Huckfeldt R (2002) The social calculus of voting: interpersonal, media, and organizational influences on presidential choices. Am Polit Sci Rev 96(1):57–73Google Scholar
  4. Clinton J, Jackman S, Rivers D (2004) The statistical analysis of roll call data. Am Polit Sci Rev 98(2): 355–370Google Scholar
  5. Deng H, Han J, Zhao B, Yu Y, Lin CX (2011) Probabilistic topic models with biased propagation on heterogeneous information networks. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining, San Diego, pp 1271–1279Google Scholar
  6. Fowler JH (2006) Connecting the congress: a study of cosponsorship networks. Polit Anal 14(4): 456–487Google Scholar
  7. Gerrish SM, Blei DM (2011) Predicting legislative roll calls from text. In: Proceedings of the international conference on machine learning, Bellevue, pp 489–496Google Scholar
  8. Hasan MA, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: Proceedings of the SIAM conference on data mining workshops, BethesdaGoogle Scholar
  9. Hinckley B (1972) Coalitions in congress: size and ideological distance. Midwest J Polit Sci 16(2):197–207Google Scholar
  10. Ji M, Han J, Danilevsky M (2011) Ranking-based classification of heterogeneous information networks. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining, San Diego, pp 1298–1306Google Scholar
  11. Kuklinski JH, Elling RC (1977) Representational role, constituency opinion, and legislative roll-call behavior. Am J Polit Sci 21(1):135–147Google Scholar
  12. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Tech 58(7):1019–1031Google Scholar
  13. Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining, Arlington, pp 243–252Google Scholar
  14. Liu S, Ying L, Shakkottai S (2010) Influence maximization in social networks: an Ising-model-based approach. In: Proceedings of the Allerton conference on communication, control and computing, Monticello, pp 570–576Google Scholar
  15. Lu Z, Savas B, Tang W, Dhillon I (2010) Supervised link prediction using multiple sources. In: Proceedings of the IEEE international conference on data mining, Sydney, pp 923–928Google Scholar
  16. Luck EM, Beaton J, Moffatt JJ (2010) The social media (r)evolution: Obama's political campaign. In: Proceedings of the Global marketing conference, TokyoGoogle Scholar
  17. Netrapalli P, Banerjee S, Sanghavi S, Shakkottai S (2010) Greedy learning of Markov network structure. In: Proceedings of the Allerton conference on communication, control and computing, Monticello, pp 1295–1302Google Scholar
  18. Pan JY, Yang HJ, Faloutsos C, Duygulu P (2004) Automatic multimedia cross-modal correlation discovery. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining, Seattle, pp 653–658Google Scholar
  19. Rice SA (1925) The political vote as a frequency distribution of opinion. J Am Stat Assoc 19(145):70–75Google Scholar
  20. Sun Y, Barber R, Gupta M, Aggarwal CC, Han J (2011) Co-author relationship prediction in heterogeneous bibliographic networks. In: Proceedings of the international conference on advances in social network analysis and mining, Kaohsiung, pp 121–128Google Scholar
  21. Taskar B, Wong MF, Abbeel P, Koller D (2004) Link prediction in relational data. In: Advances in neural information processing systems 16. MIT Press, CambridgeGoogle Scholar
  22. Tong H, Faloutsos C, Pan JY (2006) Fast random walk with restart and its applications. In: Proceedings of the IEEE international conference on data mining, Washington, pp 613–622Google Scholar
  23. Wang E, Liu D, Silva J, Dunson D, Carin L (2011) Joint analysis of time-evolving binary matrices and associated documents. In: Advances in neural information processing systems 23. MIT Press, Cambridge, pp 2370–2378Google Scholar
  24. Wang J, Varshney KR, Mojsilović A (2012) Legislative prediction via random walks over a heterogeneous graph. In: Proceedings of the SIAM international conference on data mining, Anaheim, pp 1095–1106Google Scholar
  25. Zhou D, Orshanskiy SA, Zha H, Giles CL (2007) Co-ranking authors and documents in a heterogeneous network. In: Proceedings of the IEEE international conference on data mining, Omaha, pp 739–744Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jun Wang
    • 1
  • Kush R. Varshney
    • 1
  • Aleksandra Mojsilović
    • 1
  1. 1.Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research CenterYorktown HeightsUSA