1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 3. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 4. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China 5. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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.
Nature citation relationship data including 19 fields
DBLP
4,328,431
36,032,774
Citation relationship data in the field of computer science
Galaxy
?
?
47,310 academic networks led by highly cited papers
Tab.1
Dataset
VSAN
ForceAtlas2
FMMM
Pivot MDS
GRIP
FR
KK
Feather-lastfm-social
0.2374
0.1999
0.1197
0.1206
0.0543
0.0169
0.0163
Musae-facebook
0.2946
0.2092
0.1178
0.0893
0.0479
0.0058
0.0061
Soc-Slashdot0811
0.0713
0.0525
0.0509
0.0631
0.0381
0.0349
?
Com-Amazon
0.3639
?
0.0583
0.0854
0.0324
?
?
Nature
0.3748
?
0.1327
0.2563
?
?
?
DBLP
0.0261
?
?
?
?
?
?
Tab.2
Dataset
VSAN
ForceAtlas2
FMMM
Pivot MDS
GRIP
FR
KK
Feather-lastfm-social
6.09
6.48
4.70
1.01
2.28
9.02
58.03
Musae-facebook
17.78
106.20
9.44
3.16
17.83
53.47
899.95
Soc-Slashdot0811
152.37
3765.30
41.04
22.07
180.52
703.52
?
Com-Amazon
207.17
?
162.64
47.77
1350.34
?
?
Nature
3304.79
?
818.58
558.72
?
?
?
DBLP
2128.80
?
?
?
?
?
?
Tab.3
Dataset
Graph segmentation
Subgraph layout
Structure equivalence
Equivalent structure layout
Stitching
Feather-lastfm-social
1.31
3.69
0.13
0.72
0.25
Musae-facebook
5.42
9.72
0.61
0.86
1.17
Soc-Slashdot0811
20.52
126.20
1.66
1.39
2.60
Com-Amazon
94.11
96.07
4.80
1.93
10.26
Nature
368.76
2775.98
108.84
0.76
50.46
DBLP
?
1795.84
165.47
80.54
86.95
Tab.4
Leading article of nebula graph
Nodes
Edges
SVG loading
Hierarchical slice loading
3D Convolutional Neural Networks for Human Action Recognition
1600
8252
1.66
1.10
Mastering the game of Go with deep neural networks and tree search
3035
7332
1.90
Reducing the dimensionality of data with neural networks
6781
33618
1.96
ImageNet: A large-scale hierarchical image database
8807
54767
2.65
Deep Residual Learning for Image Recognition
15405
84777
4.54
Diffusion of Innovations
34636
151439
6.88
The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations
53802
254402
11.52
Statistical Power Analysis for the Behavioral Sciences
94727
341637
17.83
Tab.5
Fig.4
Fig.5
Fig.6
Fig.7
Fig.8
Fig.9
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