Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
US Senate is the venue of political debates where the federal bills are formed and voted. Senators show their support/opposition along the bills with their votes. This information makes it possible to extract the polarity of the senators. We use signed bipartite graphs for modeling debates, and we propose an algorithm for partitioning both the senators, and the bills comprising the debate into binary opposing camps. Simultaneously, our algorithm scales both the senators and the bills on a univariate scale. Using this scale, a researcher can identify moderate and partisan senators within each camp, and polarizing vs. unifying bills. We applied our algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.