[论文解读] DeepVenn -- a web application for the creation of area-proportional Venn diagrams using the deep learning framework Tensorflow.js

Tim Hulsen|arXiv (Cornell University)|Sep 27, 2022

Data Visualization and Analytics被引用 69

一句话总结

DeepVenn 是一个 Web 应用，使用 TensorFlow.js 生成面积比例的韦恩图，最多支持十集合，输入为 ID 列表，并自动优化重叠。

ABSTRACT

Motivation: The Venn diagram is one of the most popular methods to visualize the overlap and differences between data sets. It is especially useful when it is are 'area-proportional'; i.e. the sizes of the circles and the overlaps are proportional to the sizes of the data sets. There are some tools available that can generate area-proportional Venn Diagrams, but most of them are limited to two or three circles, and others are not available as a web application or accept only numbers and not lists of IDs as input. Some existing solutions also have limited accuracy because of outdated algorithms to calculate the optimal placement of the circles. The latest machine learning and deep learning frameworks can offer a solution to this problem. Results: The DeepVenn web application can create area-proportional Venn diagrams of up to ten sets. Because of an algorithm implemented with the deep learning framework Tensorflow.js, DeepVenn automatically finds the optimal solution in which the overlap between the circles corresponds to the sizes of the overlap as much as possible. The only required input is two to ten lists of IDs. Optional parameters include the main title, the subtitle, the set titles and colours of the circles and the background. The user can choose to display absolute numbers or percentages in the final diagram. The image can be saved as a PNG file by right-clicking on it and choosing "Save image as". The right side of the interface also shows the numbers and contents of all intersections. Availability: DeepVenn is available at https://www.deepvenn.com. Contact: [e-mail]

研究动机与目标

• 用面积比例的韦恩图可视化数据集之间的重叠。
• 支持最多十个集合，并且输入为 ID 列表而不仅仅是数字。
• 利用基于深度学习的算法来优化圆的位置，使重叠反映数据规模。
• 提供可配置的美学设置（标题、颜色、背景）和输出选项（绝对数值或百分比）。
• 允许将图表另存为 PNG，并在界面上显示交集内容。

提出的方法

• 在 TensorFlow.js 中实现基于深度学习的算法，以优化圆的位置，使重叠与数据规模相对应。
• 将输入接受为两到十个 ID 列表以定义集合。
• 提供主标题、子标题、集合标题、圆圈颜色和背景的可选参数。
• 在最终图中提供绝对数值或百分比的显示模式。
• 通过右键单击“Save image as”来导出 PNG。
• 在界面上显示所有交集的数值和内容。

实验结果

研究问题

• RQ1 DeepVenn 算法在最多十个集合的情况下，如何准确地放置面积比例的圆？
• RQ2 DeepVenn 能有效处理的集合的最大数量是多少？
• RQ3 DeepVenn 接受哪些输入格式，这些如何影响可用性和准确性？
• RQ4 哪些用户自定义选项会影响所产生图的可读性和实用性？
• RQ5 DeepVenn 如何呈现并导出计算得到的交集数据？

主要发现

• 该工具可以创建最多十个集合的面积比例韦恩图。
• 使用 TensorFlow.js 的算法会自动找到一个最优放置，使重叠尽可能反映重叠大小。
• 输入为两到十个 ID 列表，且可选参数包括标题、子标题、集合标题、颜色和背景。
• 用户可以选择在最终图中显示绝对数值或百分比。
• 该图可以保存为 PNG 文件，且界面在右侧显示交集内容。