Generating Abstract Networks Using Multi-relational Biological Data

This paper presents an approach for visual exploration of groups in network data. We let users visually cluster nodes based on common semantic and relational features. We describe the clusters in the context of multi-relational protein data. Finally, we illustrate the clusters as composite nodes using a visual analytic tool and show how to create a meaningful abstracted protein network by connecting these composite nodes based on common membership or common attribute features.