OnionGraph: Hierarchical topology+attribute multivariate network visualization
Hierarchical abstraction is a scalable strategy to deal with large networks. Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology, each of which has its own advantage. Very few previous system has the capability...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-03-01
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| Series: | Visual Informatics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468502X20300024 |
| _version_ | 1818908409089490944 |
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| author | Lei Shi Qi Liao Hanghang Tong Yifan Hu Chaoli Wang Chuang Lin Weihong Qian |
| author_facet | Lei Shi Qi Liao Hanghang Tong Yifan Hu Chaoli Wang Chuang Lin Weihong Qian |
| author_sort | Lei Shi |
| collection | DOAJ |
| description | Hierarchical abstraction is a scalable strategy to deal with large networks. Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology, each of which has its own advantage. Very few previous system has the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for the exploratory visual analysis of heterogeneous multivariate networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a hierarchical combination of both. These aggregations can be split, merged and filtered under the focus+context interaction model, or automatically traversed by the information-theoretic navigation method. Node aggregations that contain subsets of nodes are displayed by the onion metaphor, indicating the level and details of the abstraction. We have evaluated the OnionGraph tool in three real-world cases. Performance experiments demonstrate that on a commodity desktop, our method can scale to million-node networks while preserving the interactivity for analysis. Keywords: Multivariate network visualization, Hierarchical abstraction, Focus+context, Entropy |
| first_indexed | 2024-12-19T22:10:33Z |
| format | Article |
| id | doaj.art-3f8b1178fb1b4f0e84ac09c41bdd676c |
| institution | Directory Open Access Journal |
| issn | 2468-502X |
| language | English |
| last_indexed | 2024-12-19T22:10:33Z |
| publishDate | 2020-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Visual Informatics |
| spelling | doaj.art-3f8b1178fb1b4f0e84ac09c41bdd676c2022-12-21T20:03:55ZengElsevierVisual Informatics2468-502X2020-03-01414357OnionGraph: Hierarchical topology+attribute multivariate network visualizationLei Shi0Qi Liao1Hanghang Tong2Yifan Hu3Chaoli Wang4Chuang Lin5Weihong Qian6ACT&BDBC, Department of Computer Science & Engineering, Beihang University, Beijing 100191, ChinaDepartment of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, United StatesDepartment of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, United StatesYahoo Labs, New York, NY 10036, United StatesDepartment of Computer Science & Engineering, University of Notre Dame, Notre Dame, IN 46556, United StatesDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaAlibaba Cloud, Beijing 100102, China; Corresponding author.Hierarchical abstraction is a scalable strategy to deal with large networks. Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology, each of which has its own advantage. Very few previous system has the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for the exploratory visual analysis of heterogeneous multivariate networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a hierarchical combination of both. These aggregations can be split, merged and filtered under the focus+context interaction model, or automatically traversed by the information-theoretic navigation method. Node aggregations that contain subsets of nodes are displayed by the onion metaphor, indicating the level and details of the abstraction. We have evaluated the OnionGraph tool in three real-world cases. Performance experiments demonstrate that on a commodity desktop, our method can scale to million-node networks while preserving the interactivity for analysis. Keywords: Multivariate network visualization, Hierarchical abstraction, Focus+context, Entropyhttp://www.sciencedirect.com/science/article/pii/S2468502X20300024 |
| spellingShingle | Lei Shi Qi Liao Hanghang Tong Yifan Hu Chaoli Wang Chuang Lin Weihong Qian OnionGraph: Hierarchical topology+attribute multivariate network visualization Visual Informatics |
| title | OnionGraph: Hierarchical topology+attribute multivariate network visualization |
| title_full | OnionGraph: Hierarchical topology+attribute multivariate network visualization |
| title_fullStr | OnionGraph: Hierarchical topology+attribute multivariate network visualization |
| title_full_unstemmed | OnionGraph: Hierarchical topology+attribute multivariate network visualization |
| title_short | OnionGraph: Hierarchical topology+attribute multivariate network visualization |
| title_sort | oniongraph hierarchical topology attribute multivariate network visualization |
| url | http://www.sciencedirect.com/science/article/pii/S2468502X20300024 |
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