Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams

TL;DR

Calliope-Net is an automated system that generates annotated node-link diagrams from graph data, helping users discover and understand social structures and facts more easily through visual analysis.

Contribution

It introduces a novel system combining fact discovery, organization, and visualization with a new layout algorithm for meaningful annotated graphs.

Findings

System effectively aids in discovering graph data facts.

Annotated visualizations improve user understanding.

User study confirms system benefits.

Abstract

Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives. Therefore, we present an automatic graph facts generation system, Calliope-Net, which consists of a fact discovery module, a fact organization module, and a visualization module. It creates annotated node-link diagrams with facts automatically discovered and organized from network data. A novel layout algorithm is designed to present meaningful and visually appealing annotated graphs. We evaluate the proposed system with two case studies and an in-lab user study. The results show that Calliope-Net can benefit users in discovering and understanding graph data facts with visually pleasing annotated visualizations.