利用LeaderRank识别有影响力的作者

Identifying Influential Authors Based on LeaderRank

【目的】识别研究领域内有影响力的作者。【方法】将Leader Rank用于合作网络测度作者影响力,通过不同的加权算法探讨合作次数和被引频次对重要作者排序的影响,在此基础上整合两个指标提出CW_LR算法,从合作和引用两个维度识别有影响力的作者。【结果】CW_LR算法与被引频次有相关性,但与被引频次或其他几种加权方法相比,识别出有影响力的作者与业界公认的更为一致。【局限】只在"信息计量学"领域进行实证,后续将该方法扩展到其他领域进一步验证其有效性。【结论】同时考虑合作关系强度和引用影响力,从合作和引用两个维度能更准确地识别出有影响力的作者。

[Objective] Provide an alternative perspective for identifying influential authors. [Methods] This paper uses the weighted LeaderRank algorithm to measure author＇s impacts in coauthorship network. Respectively validates the effects of citations and the number of cooperation on sorting influential authors through different weighted algorithms. And base on the validation a new weighted algorithm named CW_LR is proposed by integrating these two factors. [Results] CW_LR algorithm is interrelated with citations, but compared with citations or other weighted algorithms, the result of CW_LR algorithm is more consistent with expert knowledge. [Limitations] This algorithm is tested in the informetrics research community, while further effectiveness validation in other research community is required. [Conclusions] The strength of cooperation and citation impact are considered at the same time in CW_LR algorithm, and this algorithm identifies the influential author more accurately from two dimensions.