Concise provenance of interactive network analysis

Takanori Fujiwara, Tarik Crnovrsanin, Kwan-Liu Ma

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)213-224
Number of pages12
JournalVisual Informatics
Volume2
Issue number4
DOIs
StatePublished - Dec 1 2018

Keywords

  • Interactive visualization
  • Network data
  • Provenance
  • Visual analytics

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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