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VS AN:a new visualization method for super-large-scale academic networks

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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.

academic networkslarge graph visualizationgraph layoutgraph loading

Qi LI、Xingli WANG、Luoyi FU、Xinde CAO、Xinbing WANG、Jing ZHANG、Chenghu ZHOU

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Department of Electronic Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

School of Environmental Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

School of Oceanography,Shanghai Jiao Tong University,Shanghai 200240,China

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShanghai Pilot Program for Basic Research-Shanghai Jiao Tong University100-Talents Program of Xinhua News AgencyProgram of Shanghai Academic/Technology Research Leader

4205010562020106005620611460026196020600218XD1401800

2024

10.1007/s11704-022-2078-5

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2024.18(1)
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