TY - JOUR
T1 - How to Optimize an Academic Team When the Outlier Member is Leaving?
AU - Yu, Shuo
AU - Liu, Jiaying
AU - Wei, Haoran
AU - Xia, Feng
AU - Tong, Hanghang
N1 - Publisher Copyright:
© 2001-2011 IEEE.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - 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 influences 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.
AB - 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 influences 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.
UR - http://www.scopus.com/inward/record.url?scp=85097960889&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097960889&partnerID=8YFLogxK
U2 - 10.1109/MIS.2020.3042871
DO - 10.1109/MIS.2020.3042871
M3 - Article
AN - SCOPUS:85097960889
SN - 1541-1672
VL - 36
SP - 23
EP - 30
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 3
M1 - 9286424
ER -