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.

中文翻译：

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使用网络嵌入为大型文献计量网络获得更丰富，更稳定的网络布局

摘要

目的

这项研究的目的是探索基于深度学习的嵌入式模型是否可以为大型引用网络提供更好的可视化解决方案。

设计/方法/方法

我们的团队将从深度学习社区借用的可视化方法与著名的大规模数据的文献计量网络可视化进行了比较。通过使用三种网络嵌入模型以及t-SNE降维技术，可以看到47,294篇高被引论文。此外，为评估目的，使用相同的数据集创建了三个底图。所有底图均使用具有不同边缘切割策略和参数的经典OpenOrd方法。

发现

具有t-SNE的网络嵌入式地图保留了与全边经典力导向地图非常相似的全局结构，而这些地图的局部结构却有所不同。其中，Node2Vec模型具有最佳的整体可视化性能，局部结构得到了显着改善，并且地图的布局具有很高的稳定性。

研究局限性

对于网络嵌入式模型而言，训练的计算和时间成本非常高，无法获得高维潜矢量。仅测试了一种降维技术。

实际影响

本文证明了网络嵌入模型能够在向量空间中准确地重建大型文献计量网络。将来，除了网络可视化之外，许多基于向量的经典机器学习算法都可以应用于网络表示，以解决文献计量分析任务。

创意/价值

本文首次将经典科学制图可视化与基于网络嵌入的大规模数据集可视化进行了系统的比较。我们展示了基于深度学习的t-SNE网络嵌入模型可以提供更丰富，更稳定的科学地图。我们还设计了一种实用的评估方法来调查和比较地图。