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Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks

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  • Yu, Dejian
  • Sheng, Libo

Abstract

Main path analysis is a useful tool to form the backbone of a citation network by linking important connections, which has been widely used to track the knowledge diffusion paths in a specific domain. Contrary to the traditional assumption that all citations in the citation network are treated equally, this paper proposes the influence difference main path analysis model by distinguishing citations based on the prestige of citing papers. Three algorithms named search path count with PageRank (SPC-PR), search path link count with PageRank (SPLC-PR) and search path node pair with PageRank (SPNP-PR) are devised to weight the citation network in this paper. Finally, two cases, the DNA citation network and a large-scale citation network related to the blockchain domain, are investigated to examine the effectiveness of the proposed algorithms. The results show that the proposed model can not only uncover the evolutionary process appropriately, but also can effectively distinguish the citations in the network by taking the influence difference into account. This study enriches the methodology research of main path analysis and provides the scholars with practical reference for the further development of main path analysis.

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  • Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000572
    DOI: 10.1016/j.joi.2021.101186
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