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Measuring relatedness between communities in a citation network

Author

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  • Naoki Shibata
  • Yuya Kajikawa
  • Ichiro Sakata

Abstract

As academic disciplines are segmented and specialized, it becomes more difficult to capture relevant research areas precisely by common retrieval strategies using either keywords or journal categories. This paper proposes a method of measuring the relatedness among sets of academic papers in order to detect unrelated communities which are not related to target topic. A citation network, extracted by given keywords, is divided into communities based on the density of links. We measured and compared four measures of relatedness between two communities in a citation network for three large‐scale citation datasets. We used both link and semantic similarities. The topological distance from the center in a citation network is a more efficient measure for removing the unrelated communities than the other three measures: the ratio of the number of intercluster links over the all links, the ratio of the number of common terms over all terms, cosine similarity of tf‐idf vectors.

Suggested Citation

  • Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2011. "Measuring relatedness between communities in a citation network," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1360-1369, July.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:7:p:1360-1369
    DOI: 10.1002/asi.21477
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    Citations

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    Cited by:

    1. Wang, Ming-Yeu & Fang, Shih-Chieh & Chang, Yu-Hsuan, 2015. "Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 182-195.
    2. Ogawa, Takaya & Kajikawa, Yuya, 2017. "Generating novel research ideas using computational intelligence: A case study involving fuel cells and ammonia synthesis," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 41-47.
    3. Kajikawa, Yuya & Mejia, Cristian & Wu, Mengjia & Zhang, Yi, 2022. "Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    4. Ogawa, Takaya & Kajikawa, Yuya, 2015. "Assessing the industrial opportunity of academic research with patent relatedness: A case study on polymer electrolyte fuel cells," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 469-475.
    5. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    6. Shen, Yung-Chi & Wang, Ming-Yeu & Yang, Ya-Chu, 2020. "Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    7. Yuya Kajikawa, 2022. "Reframing evidence in evidence-based policy making and role of bibliometrics: toward transdisciplinary scientometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5571-5585, September.
    8. Ichiro Tsuchimoto & Yuya Kajikawa, 2022. "Recycling of Plastic Waste: A Systematic Review Using Bibliometric Analysis," Sustainability, MDPI, vol. 14(24), pages 1-39, December.

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