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CompositeView: A Network-Based Visualization Tool
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 speci...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281616/ https://www.ncbi.nlm.nih.gov/pubmed/35847767 http://dx.doi.org/10.3390/bdcc6020066 |
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author | Allegri, Stephen A. McCoy, Kevin Mitchell, Cassie S. |
author_facet | Allegri, Stephen A. McCoy, Kevin Mitchell, Cassie S. |
author_sort | Allegri, Stephen A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9281616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-92816162022-07-14 CompositeView: A Network-Based Visualization Tool Allegri, Stephen A. McCoy, Kevin Mitchell, Cassie S. Big Data Cogn Comput Article 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. 2022-06 2022-06-14 /pmc/articles/PMC9281616/ /pubmed/35847767 http://dx.doi.org/10.3390/bdcc6020066 Text en https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). (https://creativecommons.org/licenses/by/4.0/) ) |
spellingShingle | Article Allegri, Stephen A. McCoy, Kevin Mitchell, Cassie S. CompositeView: A Network-Based Visualization Tool |
title | CompositeView: A Network-Based Visualization Tool |
title_full | CompositeView: A Network-Based Visualization Tool |
title_fullStr | CompositeView: A Network-Based Visualization Tool |
title_full_unstemmed | CompositeView: A Network-Based Visualization Tool |
title_short | CompositeView: A Network-Based Visualization Tool |
title_sort | compositeview: a network-based visualization tool |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281616/ https://www.ncbi.nlm.nih.gov/pubmed/35847767 http://dx.doi.org/10.3390/bdcc6020066 |
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