论文标题
当离群成员离开时，如何优化学术团队？
How to optimize an academic team when the outlier member is leaving?
论文作者
论文摘要
一个学术团队是一个高度粘性的学者小组，该小组被认为是改善质量和数量方面的科学产量的有效方法。但是，高员工流动率带来了一系列可能会对团队绩效产生负面影响的问题。为了应对这一挑战，我们首先检测可能会离开的成员的趋势。在这里，离群值是根据熟悉度定义的，这是通过使用协作强度来量化的。假设团队成员对团队外的学者有更高的熟悉程度，那么该成员可能可能会离开团队。为了最大程度地减少离开这种异常值成员所致的影响，我们提出了一种优化解决方案，以找到可以替代离群成员的合适候选人。基于与图内的随机步行，我们的解决方案涉及熟悉度匹配，技能匹配以及结构匹配。提出的方法被证明是有效的，并且在应用于计算机科学学术团队时胜过现有的方法。
An academic team is a highly-cohesive collaboration group of scholars, which has been recognized as an effective way to improve scientific output in terms of both quality and quantity. However, the high staff turnover brings about a series of problems that may have negative influence on team performance. To address this challenge, we first detect the tendency of the member who may potentially leave. Here the outlierness is defined with respect to familiarity, which is quantified by using collaboration intensity. It is assumed that if a team member has a higher familiarity with scholars outside the team, then this member might probably leave the team. To minimize the influence caused by the leaving of such an outlier member, we propose an optimization solution to find a proper candidate who can replace the outlier member. Based on random walk with graph kernel, our solution involves familiarity matching, skill matching, as well as structure matching. The proposed approach proves to be effective and outperforms existing methods when applied to computer science academic teams.
