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

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.

Guande Wu (吴冠德)

CS Ph.D. Student

Hi! This is Guande Wu, a Ph.D. student in Tandon School of Engineering, New York University. My advisor is Prof. Claudio T. Silva and I am also working with Prof. Chen Zhao. My research interest mainly lies in the human-AI collaboration especially in AR scenario. Previously, I have worked with many outstanding experts in visualization and software engineering at Zhejiang University, Tongji University, UC Davis and Microsoft Research Asia and Adobe Research.