Concise provenance of interactive network analysis

Abstract Large, complex networks are commonly found in many application domains, such as sociology, biology, and software engineering. Analyzing such networks can be a non-trivial task, as it often takes many interactions to derive a finding. It is thus beneficial to capture and summarize the important steps in an analysis. This provenance would then effectively support recalling, reusing, reproducing, and sharing the analysis process and results. However, the provenance of analyzing a large, complex network would often be a long interaction record. To automatically compose a concise visual summarization of network analysis provenance, we introduce a ranking model together with a reduction algorithm. The model identifies and orders important interactions used in the network analysis. Based on this model, our algorithm is able to minimize the provenance, while still preserving all the essential steps for recalling and sharing the analysis process and results. We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.