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【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。
【Objective】 The paper identifies the team’s high-cooperation scholars and their dynamic academic influence model by using the high-cooperation scholar recognition algorithm and scholar’s pattern recognition algorithm to provide reference for the growth of the team. 【Method】 According to the situation of the number of co-operation scholars, the high-cooperation scholars in the team were distinguished; the academic influence of the scholar and the academic influence of the scholar were measured by the co-authors’ Force mode. 【Result】 The number of high-cooperation scholars in different teams varies from zero to many. Highly cooperative scholars have different dynamic academic influence patterns and are identified as steady growth or mature volatility patterns. Limitations Only two indicators are used to measure the influence of scholars. For more complicated scholars, more indicators need to be introduced to identify their dynamic academic influence patterns. 【Conclusion】 High-cooperative scholar recognition algorithm and scholar pattern recognition algorithm can reasonably identify high-cooperation scholars and their dynamic academic influence patterns in a team.