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| Hierarchical focus+context heterogeneous network visualization | |
| Shi, Lei (1); Liao, Qi (2); Tong, Hanghang (3); Hu, Yifan (4); Zhao, Yue (5); Lin, Chuang (5) | |
| 2014 | |
| Conference Name | 2014 7th IEEE Pacific Visualization Symposium, PacificVis 2014 |
| Pages | 89-96 |
| Conference Date | March 4, 2014 - March 7, 2014 |
| Conference Place | Yokohama, Kanagawa, Japan |
| Indexed Type | EI |
| Publish Place | IEEE Computer Society |
| ISSN | 21658765 |
| ISBN | 9781479928736 |
| Department | (1) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China; (2) Department of Computer Science, Central Michigan University, United States; (3) Computer Science Department, City College, CUNY, United States; (4) ATandT Labs Research, Germany; (5) Tsinghua University, China |
| English Abstract | Aggregation is a scalable strategy for dealing with large network data. Existing network visualizations have allowed nodes to be aggregated based on node attributes or network topology, each of which has its own advantages. However, very few previous systems have the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for exploratory visual analysis of large heterogeneous networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a mixture of both. Subsets of nodes can be flexibly split and merged under the hierarchical focus+context interaction model, supporting sophisticated analysis of the network data. Node aggregations that contain subsets of nodes are displayed with multiple concentric circles, or the onion metaphor, indicating how many levels of abstraction they contain. We have evaluated the OnionGraph tool in two real-world cases. Performance experiments demonstrate that on a commodity desktop, OnionGraph can scale to million-node networks while preserving the interactivity for analysis. © 2014 IEEE.; Aggregation is a scalable strategy for dealing with large network data. Existing network visualizations have allowed nodes to be aggregated based on node attributes or network topology, each of which has its own advantages. However, very few previous systems have the capability to enjoy the best of both worlds. This paper presents OnionGraph, an integrated framework for exploratory visual analysis of large heterogeneous networks. OnionGraph allows nodes to be aggregated based on either node attributes, topology, or a mixture of both. Subsets of nodes can be flexibly split and merged under the hierarchical focus+context interaction model, supporting sophisticated analysis of the network data. Node aggregations that contain subsets of nodes are displayed with multiple concentric circles, or the onion metaphor, indicating how many levels of abstraction they contain. We have evaluated the OnionGraph tool in two real-world cases. Performance experiments demonstrate that on a commodity desktop, OnionGraph can scale to million-node networks while preserving the interactivity for analysis. © 2014 IEEE. |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16582 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Shi, Lei ,Liao, Qi ,Tong, Hanghang ,et al. Hierarchical focus+context heterogeneous network visualization[C]. IEEE Computer Society,2014:89-96. |
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