Vote trading, also commonly known as logrolling, is a cornerstone of the economic analysis of politicians’ be- havior in legislatures and parliamentary commissions. The phenomenon can have substantial effects on economic policy making. However, as the trade of votes between legislators is not directly observable, finding empirical evidence for logrolling with traditional econometric methods is very challanging. In this project, we develop a novel empirical strategy to detect legislative vote trading using new data and novel computational methods that identify vote traders through reciprocal behavior in directed weighted networks. Our project takes advantage of recently developed methods to compile vast data sets on big public data as well as novel insights from network science. It lays thus in the intersection of political economics, network science, and computational social science. In principle, our approach can be applied to a broad variaty of roll call data, and refined with different models of reciprocity. It allows us, for example, to study how the hidden cooperation between legislators is affected by different institutional settings or how resilient a vote trading network is to external shocks such as the removal of a legislator who unexpectedly leaves politics. The flexibility of our method is expected to shed new light on many aspects of legislative logrolling.