Zhao Xin, Wang Xuan, Zou Xianzhe, Liang Huiming, Bai Genghuai, Zhang Ning, Huang Xin, Zhou Fangfang, Zhao Ying
School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.
QAX Security Center, Qi An Xin Technology Group Inc., Beijing, 100044, China.
Vis Comput Ind Biomed Art. 2025 May 8;8(1):12. doi: 10.1186/s42492-025-00193-y.
Web-based libraries, such as D3.js, ECharts.js, and G6.js, are widely used to generate node-link graph visualizations. These libraries allow users to call application programming interfaces (APIs) without identifying the details of the encapsulated techniques such as graph layout algorithms and graph rendering methods. Efficiency requirements, such as visualizing a graph with 3k nodes and 4k edges within 1 min at a frame rate of 30 fps, are crucial for selecting a proper library because libraries generally present different characteristics owing to the diversity of encapsulated techniques. However, existing studies have mainly focused on verifying the advantages of a new layout algorithm or rendering method from a theoretical viewpoint independent of specific web-based libraries. Their conclusions are difficult for end users to understand and utilize. Therefore, a trial-and-error selection process is required. This study addresses this gap by conducting an empirical experiment to evaluate the performance of web-based libraries. The experiment involves popular libraries and hundreds of graph datasets covering node scales from 100 to 200k and edge-to-node ratios from 1 to 10 (including complete graphs). The experimental results are the time costs and frame rates recorded using the libraries to visualize the datasets. The authors analyze the performance characteristics of each library in depth based on the results and organize the results and findings into application-oriented guidelines. Additionally, they present three usage cases to illustrate how the guidelines can be applied in practice. These guidelines offer user-friendly and reliable recommendations, aiding users in quickly selecting the desired web-based libraries based on their specific efficiency requirements for node-link graph visualizations.
基于网络的库，如D3.js、ECharts.js和G6.js，被广泛用于生成节点链接图可视化。这些库允许用户调用应用程序编程接口（API），而无需了解封装技术的细节，如图形布局算法和图形渲染方法。效率要求，例如以30帧/秒的帧率在1分钟内可视化一个包含3000个节点和4000条边的图，对于选择合适的库至关重要，因为由于封装技术的多样性，各库通常呈现出不同的特性。然而，现有研究主要集中在从独立于特定基于网络的库的理论角度验证新布局算法或渲染方法的优势。它们的结论对于终端用户来说难以理解和利用。因此，需要一个反复试验的选择过程。本研究通过进行实证实验来评估基于网络的库的性能，以填补这一空白。该实验涉及流行的库和数百个图形数据集，涵盖从100到200k的节点规模以及从1到10的边与节点比率（包括完全图）。实验结果是使用这些库可视化数据集时记录的时间成本和帧率。作者根据结果深入分析每个库的性能特征，并将结果和发现整理成面向应用的指南。此外，他们还给出了三个使用案例来说明这些指南如何在实践中应用。这些指南提供了用户友好且可靠的建议，帮助用户根据其对节点链接图可视化的特定效率要求快速选择所需的基于网络的库。
