基于合作网络的学者动态学术影响力模式识别研究

发布时间：2018-02-16 14:58

本文关键词： 学术影响力社会网络分析动态模式　出处：《数据分析与知识发现》2017年04期 　论文类型：期刊论文

【摘要】：【目的】利用高合作学者识别算法和学者影响力模式识别算法识别出团队的高合作学者以及其动态学术影响力模式,为团队中人才成长提供参考。【方法】根据学者的合作人数情况,区分出团队中的高合作学者;利用高合作学者的发文量和度数中心度指标测度学者的个人影响力和在团队的影响力,识别学者的动态学术影响力模式。【结果】不同团队中的高合作学者数量不一,为零至多个。高合作学者的动态学术影响力模式不同,识别为稳步增长或成熟波动模式。【局限】仅利用两个指标来测度学者影响力,对于较复杂情况的学者需引入更多的指标识别其动态学术影响力模式。【结论】高合作学者识别算法和学者影响力模式识别算法能够较合理地识别出团队中的高合作学者及其动态学术影响力模式。
[Abstract]:[objective] to identify the high cooperative scholar and its dynamic academic influence pattern by using the high cooperative scholar recognition algorithm and the scholar influence pattern recognition algorithm. [methods] according to the cooperation number of scholars, the high cooperative scholars in the team can be distinguished. Using the index of high cooperative scholars' writing quantity and degree of centrality to measure the individual influence of scholars and their influence in teams, the dynamic academic influence model of scholars is identified. [results] the number of high cooperative scholars in different teams is different. High cooperative scholars have different models of dynamic academic influence, which are identified as steady growth or mature fluctuations. [limitation] only two indicators are used to measure the influence of scholars. It is necessary to introduce more indexes to identify the dynamic academic influence pattern for the more complicated scholars. [conclusion] the high cooperative scholar recognition algorithm and the scholar influence pattern recognition algorithm can reasonably identify the high match in the team. [conclusion] the high cooperative scholar recognition algorithm and the scholar influence pattern recognition algorithm can reasonably identify the high degree of academic influence in the team. As a scholar and its dynamic academic influence model.
【作者单位】：中国科学技术信息研究所;中国国防科技信息中心;
【分类号】：G353.1

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