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Network model of knowledge diffusion

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Abstract

This paper introduces a diffusion network model: an individual-citation-based directed network model with a time dimension, as a potentially useful approach to capture the diffusion of research topics. The approach combines social network analysis, network visualization and citation analysis to discuss some of the issues concerning the spread of scientific ideas. The process of knowledge diffusion is traced from a network point of view. Using research on the h-index as a case study, we built detailed networks of individual publications and demonstrated the feasibility of applying the diffusion network model to the spread of a research. The model shows the specific paths and associations of individual papers, and potentially complementing issues raised by epidemic models, which primarily deal with average properties of entire scientific communities. Also, based on the citation-based network, the technique of main path analysis identified the articles that influenced the research for some time and linked them into a research tradition that is the backbone of the h-index field.

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Notes

  1. By searching the term “h-index” we got 189 publications. 19 out of them are not papers of “h-index” related in bibliometric. So, the 19 papers were deleted from the set of publications, leaving 170 publications.

  2. A link in this citation network is directed from a cited paper to a citing one.

  3. In our example, the number of neighbors of a given vertex is equal to all degree of this vertex.

  4. A path is a walk in which no vertex or arcs in between the source and the sink vertex occurs more than once.

  5. In an acyclic network, a source vertex is a vertex with zero indegree. In our case, a source vertex refers to an article that is not citing within the dataset.

  6. In an acyclic network, a sink vertex is a vertex with zero outdegree. In our case, a sink vertex refers to an article that is not cited within the dataset.

  7. We tried to apply a co-word analysis to the keywords to create a 2-D graph with sub-domains representing topic clusters. However, in our case, the frequencies of keywords are so small that there are too many zeros in the co-occurrence matrix. Thus, the effect of clustering is not significant. Finally, we decided to combine the contents of the 170 publications and the contribution of Alonso et al. (2009) with high occurrence keywords to get a rough view of hot sub-domains of h-index research.

  8. Here, citations refer to forward citation, that is, a paper cites papers that appeared earlier.

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Acknowledgments

This research is funded by National Natural Science Foundation of China (Projects No. 71103078), Natural Science Foundation of the Inner Mongolia Autonomous Region (Projects No. 2011BS1002), Research Program of Humanities and Social Sciences at Universities of Inner Mongolia Autonomous Region (Projects No. NJSY11009), and SPH-IMU (Projects No. Z20090115). Authors are grateful for the valuable comments and suggestions of anonymous reviewers and the editors, which significantly improved the paper. We also thank Prof. Rousseau, for their helpful discussions and English corrections of the paper.

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Authors and Affiliations

  1. School of Economics and Management, Inner Mongolia University, Hohhot, People’s Republic of China

    Xia Gao

  2. School of Management, Graduate University, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Jiancheng Guan

  3. School of Management, Fudan University, Shanghai, People’s Republic of China

    Jiancheng Guan

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  1. Xia Gao
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  2. Jiancheng Guan
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Correspondence to Xia Gao.

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Gao, X., Guan, J. Network model of knowledge diffusion. Scientometrics 90, 749–762 (2012). https://doi.org/10.1007/s11192-011-0554-z

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