VSAN: A new visualization method for super-large-scale academic networks
文献类型：期刊论文
| 作者 | Li, Qi1; Wang, Xingli2; Fu, Luoyi2; Cao, Xinde3; Wang, Xinbing1; Zhang, Jing4; Zhou, Chenghu5 |
| 刊名 | FRONTIERS OF COMPUTER SCIENCE
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 18期号:1页码:19 |
| 关键词 | academic networks large graph visualization graph layout graph loading |
| ISSN号 | 2095-2228 |
| DOI | 10.1007/s11704-022-2078-5 |
| 通讯作者 | Wang, Xinbing(xwang8@sjtu.edu.cn) |
| 英文摘要 | As a carrier of knowledge, papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology. With the booming development of science and technology, the number of papers has been growing exponentially. Just like the fact that Internet of Things (IoT) allows the world to be connected in a flatter way, how will the network formed by massive academic papers look like? Most existing visualization methods can only handle up to hundreds of thousands of node size, which is much smaller than that of academic networks which are usually composed of millions or even more nodes. In this paper, we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks (VSAN). Nodes can represent papers or authors while the edges means the relation (e.g., citation, coauthorship) between them. In order to comprehensively improve the visualization effect, three levels of optimization are taken into account in the whole design of VSAN in a progressive manner, i.e., bearing scale, loading speed, and effect of layout details. Our main contributions are two folded: 1) We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts, thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities. 2) We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels, with the ability to quickly zoom between different levels. In addition, we propose a "jumping between nebula graphs" method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks. Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes. The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously, but also returns some interesting observations that may drive extra attention from scientists. |
| WOS关键词 | LARGE GRAPHS |
| 资助项目 | National Natural Science Foundation of China[42050105] ; National Natural Science Foundation of China[62020106005] ; National Natural Science Foundation of China[62061146002] ; National Natural Science Foundation of China[61960206002] ; Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University ; 100-Talents Program of Xinhua News Agency ; Program of Shanghai Academic/Technology Research Leader[18XD1401800] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001048565800002 |
| 出版者 | HIGHER EDUCATION PRESS |
| 资助机构 | National Natural Science Foundation of China ; Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University ; 100-Talents Program of Xinhua News Agency ; Program of Shanghai Academic/Technology Research Leader |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/196327]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Xinbing |
| 作者单位 | 1.Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China 2.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China 3.Shanghai Jiao Tong Univ, Sch Environm Sci & Engn, Shanghai 200240, Peoples R China 4.Shanghai Jiao Tong Univ, Sch Oceanog, Shanghai 200240, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Qi,Wang, Xingli,Fu, Luoyi,et al. VSAN: A new visualization method for super-large-scale academic networks[J]. FRONTIERS OF COMPUTER SCIENCE,2024,18(1):19. |
| APA | Li, Qi.,Wang, Xingli.,Fu, Luoyi.,Cao, Xinde.,Wang, Xinbing.,...&Zhou, Chenghu.(2024).VSAN: A new visualization method for super-large-scale academic networks.FRONTIERS OF COMPUTER SCIENCE,18(1),19. |
| MLA | Li, Qi,et al."VSAN: A new visualization method for super-large-scale academic networks".FRONTIERS OF COMPUTER SCIENCE 18.1(2024):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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