The visualization of bipartite graphs has only recently gained some interest, as more and more application fields begin to use this special graph class as a natural way to encode their data. We present a table-based approach for the visualization of real world bipartite graphs, which may be large in size and contain a multitude of attributes. To explore complex network motifs and perform intricate selections within such graph data, we introduce a new script-based brushing mechanism which integrates naturally with the tabular graph representation. A prototype for exploring bipartite graphs, which uses the proposed visualization and interaction techniques, is also presented and used on example data sets of biochemical reaction networks.
