Hierarchical focus+context heterogeneous network visualization

ISCAS OpenIR

	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
会议名称	2014 7th IEEE Pacific Visualization Symposium, PacificVis 2014
页码	89-96
会议日期	March 4, 2014 - March 7, 2014
会议地点	Yokohama, Kanagawa, Japan
收录类别	EI
出版地	IEEE Computer Society
ISSN	21658765
ISBN	9781479928736
部门归属	(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
摘要	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.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16582
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Shi, Lei ,Liao, Qi ,Tong, Hanghang ,et al. Hierarchical focus+context heterogeneous network visualization[C]. IEEE Computer Society,2014:89-96.