VSAN: A new visualization method for super-large-scale academic networks

Abstract

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.

Graphical abstract

Keywords

academic networks / large graph visualization / graph layout / graph loading

Qi Li received his BE degree in Information and Communication Engineering from Xidian University, China in 2019. He is currently pursuing his PhD degree in Department of Electronic Engineering in Shanghai Jiao Tong University, China. His research interests are big data and machine learning

Xingli Wang received his BE degree in computer science and technology from Shanghai Jiao Tong University, China in 2022. Currently, he is pursuing his MS degree in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interest is data mining

Luoyi Fu received the BE degree in Electronic Engineering from Shanghai Jiao Tong University, China in 2009 and the PhD degree in computer science and engineering in the same university, in 2015. She is currently working as an assistant professor in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. Her research of interests are in the area of scaling laws analysis in wireless networks, connectivity analysis, sensor networks and social networks

Xinde Cao received the MS degree in Analytical Chemistry from University of Science and Technology of China, China in 1995 and the PhD degree in Analytical Chemistry in the same university in 1998. Currently, he is a professor in the School of Environmental Science and Engineering, Shanghai Jiao Tong University, China

Xinbing Wang received the BS degree from the Department of Automation, Shanghai Jiao Tong University, China in 1998, the MS degree from the Department of Computer Science and Technology, Tsinghua University, China in 2001, and the PhD degree, major from the Department of Electrical and Computer Engineering, minor from the Department of Mathematics, North Carolina State University, USA in 2006. Currently, he is a professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China. He has been an associate editor for the IEEE/ACM Transactions on Networking and the IEEE Transactions on Mobile Computing, and the member of the technical program committees of several conferences including ACM MobiCom 2012, ACM MobiHoc 2012-2014, and IEEE INFOCOM 2009-2017

Jing Zhang received the BS degree in geochemistry from Nanjing University, China in 1982, the MS degree from the Department of Marine Geology, Shandong College of Oceanography, China in 1985, and the PhD degree in geochemistry from Pierre and Marie Curie University, France in 1988. Currently, he is a professor in the School of Oceanography, Shanghai Jiao Tong University, China

Chenghu Zhou received the BS degree in geography from Nanjing University, China in 1984, and the MS and PhD degrees in geographic information system from the Chinese Academy of Sciences (CAS), China in 1987 and 1992, respectively. He is currently an academician with CAS, where he is also a research professor with the Institute of Geographical Sciences and Natural Resources Research, and a professor with the School of Geography and Ocean Science, Nanjing University, China. His research interests include spatial and temporal data mining, geographic modeling, hydrology and water resources, and geographic information systems and remote sensing applications

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Acknowledgements

This work was supported by the National Natural Science Foundation of China ( Grant Nos. 42050105, 62020106005, 62061146002, 61960206002), the Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University, the 100-Talents Program of Xinhua News Agency, and the Program of Shanghai Academic/Technology Research Leader (No. 18XD1401800).

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