Legislative Vote Prediction using Campaign Donations and Fuzzy Hierarchical Communities

Abstract

An important aspect of social networks is the discovery and partitioning of the complex graphs into dense sub-networks referred to as communities. The goal of such partitioning is to find groups who have similar attributes or behaviors. In the realm of politics, it is possible to group individuals with similar political behavior by analyzing campaign finance records. In this paper we use fuzzy hierarchical spectral clustering to find communities with campaign finance networks. Multiple experiments were performed using varying edge weighting, number and type of communities, as well as analyzing multiple different years of voting data. The results show that using the full hierarchy of community assignments for legislators is highly predictive of voting behavior in the US House of Representatives and Senate.