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

Tingting Chen, Guopeng Li, Qiping Deng, Xiaomei Wang
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引用次数: 1

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.
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利用网络嵌入技术实现大型文献计量网络更丰富更稳定的网络布局
摘要目的探讨基于深度学习的嵌入式模型能否为大型引文网络提供更好的可视化解决方案。我们的团队将借鉴深度学习社区的可视化方法与著名的用于大规模数据的文献计量网络可视化方法进行了比较。采用三种网络嵌入模型和t-SNE降维技术对47294篇高被引论文进行了可视化处理。此外,为了评估目的,使用相同的数据集创建了三个基本地图。所有的底图都使用了经典的OpenOrd方法,并采用了不同的切边策略和参数。结果发现,具有t-SNE的网络嵌入图与经典的全边力定向图保持了非常相似的全局结构,但在局部结构上存在差异。其中,Node2Vec模型整体可视化性能最好,局部结构得到显著改善,地图布局稳定性非常高。网络嵌入式模型要获得高维潜在向量,其训练的计算量和时间成本非常高。只测试了一种降维技术。本文证明了网络嵌入模型能够在向量空间中精确地重建大型文献计量网络。在未来,除了网络可视化,许多经典的基于向量的机器学习算法可以应用于解决文献计量分析任务的网络表示。本文首次对经典科学地图可视化与基于网络嵌入的大数据集可视化进行了系统比较。结果表明,基于深度学习的t-SNE网络嵌入模型可以提供更丰富、更稳定的科学图谱。我们还设计了一种实用的评价方法来调查和比较地图。
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