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Mapping the topic evolution using citation-topic model and social network analysis

机译：使用引文主题模型和社交网络分析映射主题演进

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This paper introduces an analysis framework to track the evolution of research topics based on the content including topic evolution trend, main evolution path and content changes over time. After the topics were identified by the citation-topic model, we first built a 2-mode co-occurrence (the keywords and topic co-occurrence) network to track the dynamics of topic trends. Then we built a single-mode network to illustrate the evolution path and content changes of research topics, and within which each node represented a topic and each edge indicated the relationship between topics. The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics, which helps us to better obtain the evolution trend, the main evolution path and the content change of topics. In addition, the analysis framework used in this study can reveal more micro topic evolution details. The experimental results show that this analysis framework can be used to track the evolution of research topics at a micro level and the social network analysis method used in this paper can help to grasp the topic evolution path and content changes. However, the analysis framework will produce limited results when conducting unstructured data other than the bibliographic data. In addition, the effectiveness of the framework introduced in this paper needs to be verified in more research fields. Surely, this analysis framework introduced in this paper can help scholars and researchers map the evolution process of research topics and grasp the content changes in a field's research topics over time.
机译：本文介绍了一种分析框架，以跟踪研究主题的演变，基于包括主题演进趋势，主要演化路径和内容随着时间的推移而变化。通过引文模型确定主题后，我们首先建立了一个2模式的共同发生（关键字和主题共同发生）网络，以跟踪主题趋势的动态。然后我们建立了一个单模网络，以说明研究主​​题的演进路径和内容变化，并且每个节点都表示主题，每个边缘都表示主题之间的关系。本文定义了衡量主题关联强度的索引反映了主题之间的相似性和不相似性，这有助于我们更好地获得进化趋势，主要的演化路径和主题内容变更。此外，本研究中使用的分析框架可以揭示更多的微观主题进化细节。实验结果表明，该分析框架可用于跟踪微观水平的研究主题的演变，本文中使用的社交网络分析方法可以帮助掌握主题进化路径和内容变化。然而，分析框架将在除参考书目数据以外的非结构化数据时产生有限的结果。此外，本文介绍的框架的有效性需要在更多的研究领域进行核实。当然，本文介绍的这种分析框架可以帮助学者和研究人员映射研究主题的演变过程，并掌握了现场研究主题的内容变化。

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