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.

翻译：图或网络数据在数据挖掘与可视化领域被广泛研究，用以审视不同实体与群体之间的关系。从图可视化分析中提取的数据事实对于理解复杂数据的社会结构至关重要，尤其是在数据新闻领域。然而，由于图数据的复杂性以及图叙事解读的困难性，数据新闻工作者难以自主发现图数据事实并围绕有意义的主题手动组织相关事实。为此，我们提出了一种自动图事实生成系统Calliope-Net，该系统包含事实发现模块、事实组织模块与可视化模块。它能够创建带有注释的节点链接图，其中的事实通过算法自动从网络数据中发掘并组织。我们设计了一种新颖的布局算法，以呈现有意义且视觉美观的注释图。通过两项案例研究与一项实验室用户研究，我们对所提系统进行了评估。结果表明，Calliope-Net能够通过视觉优雅的注释可视化，帮助用户发现并理解图数据事实。