Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network

doi:10.2478/jdis-2021-0006

Abstract

Abstract:

Purpose: The goal of this study is to explore whether deep learning based embedded models can provide a better visualization solution for large citation networks.
Design/methodology/approach: Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters.
Findings: The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps’ layout has very high stability.
Research limitations: The computational and time costs of training are very high for network embedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested.
Practical implications: This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliometric analysis tasks.
Originality/value: This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer, more stable science map. We also designed a practical evaluation method to investigate and compare maps.

Key words: Scientometrics, Visualization, Essential science indicators, Bibliometric networks, Network embedding, Science mapping

Chen, Ting, Guopeng Li, Qiping Deng, and Xiaomei Wang. "Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network." Journal of Data and Information Science, vol.6, no.1, 2021, pp.154-177. DOI: 10.2478/jdis-2021-0006

Figures/Tables 10

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Figure 2.

Figure 3.

Table 1

Figure 4.

Table 2

Detail of top 10 ESI fronts of each discipline overlaid in Figure 5.

ID&Field	Research front keywords	#paper
993, Social sciences, general	Police agency directed randomized field trial; police legitimacy invariant; police legitimacy; police officer body-worn cameras (bwcs)	30
39, Clinical medicine	Triple-negative essential thrombocythemia patients; myeloproliferative neoplasm-associated myelofibrosis;primary myelofibrosis	50
514, Computer science	Multigranulation fuzzy decision-theoretic rough set; triangular fuzzy decision-theoretic rough sets; intuitionistic fuzzy decision-theoretic rough sets; multigranulation decision-theoretic rough sets;	47
108, Physics	Frozen quantum coherence;measuring quantum coherence; genuine quantum coherence; quantum coherence; quantum thermodynamics	49
90, Biology & biochemistry	Reversible m(6)a rna methylation; rna m(6)a methylation; m(6)a rna methylation; m(6)a rna modification controls cell fate transition; viral m(6)a rna methylomes	42
309, Engineering	Particle swarm optimization-based maximum power point tracking algorithm; maximum power point tracking control techniques;maximum power point tracking (mppt) techniques;observe (p&o) maximum power point tracking (mppt) algorithm;maximum power point tracking techniques	36
69, Geosciences	Greenland ice sheet surface mass balance contribution; 1900-2015 greenland ice sheet surface mass balance; potential antarctic ice sheet retreat driven;northeast greenland ice sheet triggered;simple antarctic ice sheet model	43
63, Chemistry	3d porous crystalline polyimide covalent organic frameworks;crystalline 2d covalent organic frameworks; homochiral 2d porous covalent organic frameworks; large-pore crystalline polyimide covalent organic frameworks;highly crystalline covalent organic frameworks	45
107, Materials science	Equiatomic high-entropy alloy crmnfeconi; equiatomic high-entropy alloys; single-phase high-entropy alloy crmnfeconi; nanocrystalline cocrfemnni high-entropy alloy; single-phase high-entropy alloys	37
71, Space science	Small kepler planets; kepler transit candidates xvii; potentially habitable planets orbiting m dwarfs estimated; earth-size planets orbiting sun-like stars	31

Table 2

Table 3

Figure 5.

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Figure 7.

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Model	Building context of nodes	Learning method
DeepWalk	Truncated Random Walks	Skip-gram & Hierarchical Softmax
Line	1-hop and 2-hop Neighbors	Skip-gram & Negative Sampling
Node2Vec	Biased Truncated Random Walks	Skip-gram & Negative Sampling

Number of RF	Co-citation map A	Co-citation map B	Co-citation map C	N2V & tSNE	DW & tSNE	Line & tSNE
10	0.807	0.71	0.73	0.955	0.839	0.637
50	0.633	0.58	0.6	0.721	0.543	0.438
100	0.589	0.52	0.56	0.656	0.437	0.370
200	0.529	0.43	0.42	0.570	0.353	0.306
500	0.407	0.29	0.29	0.438	0.193	0.190
1,000	0.302	0.18	0.27	0.313	0.047	0.054
10,096	-0.227	-0.26	-0.26	-0.279	-0.469	-0.455