Allegri Stephen A, McCoy Kevin, Mitchell Cassie S
Laboratory for Pathology Dynamics, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Big Data Cogn Comput. 2022 Jun;6(2). doi: 10.3390/bdcc6020066. Epub 2022 Jun 14.
Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi.
大型网络对于生物信息学、知识图谱、社交网络分析和基于图的学习至关重要。CompositeView是一个基于Python的开源应用程序，它改进了交互式复杂网络可视化并提取可操作的见解。CompositeView利用特定格式的输入数据来计算综合得分，并使用Dash的Cytoscape组件显示这些得分。综合得分是概念上相似的较小数据集的定义表示，当组合在一起时会生成一个单一得分以减少信息过载。可视化的交互式结果可通过诸如节点值和边权重滑块以及图形操作选项（例如节点颜色和布局分布）等过滤元素由用户进行优化。CompositeView与其他网络可视化工具的主要区别在于，当用户交互式过滤或聚合数据时，它能够自动计算和自动更新综合得分。CompositeView是为可视化网络相关性排名而开发的，但它在处理非网络数据时也表现良好。展示了三个不同的CompositeView用例：来自SemNet 2.0的相关性排名，SemNet 2.0是一个用于基于生物医学文献发现的开源知识图谱关系排名软件；人类发展指数（HDI）数据；以及弗雷明汉姆心血管研究。对CompositeView进行了压力测试，以构建定义有效可视化数据的广度和大小的参考基准。最后，将CompositeView与Excel、Tableau、Cytoscape、neo4j、NodeXL和Gephi进行了比较。
