- Title
- How to optimize an academic team when the outlier member is leaving?
- Creator
- Yu, Shuo; Liu, Jiaying; Wei, Haoran; Xia, Feng; Tong, Hanghang
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/178795
- Identifier
- vital:15485
- Identifier
-
https://doi.org/10.1109/mis.2020.3042871
- Identifier
- ISBN:1541-1672
- Abstract
- 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.
- Relation
- IEEE Intelligent Systems Vol. 36, no. 3 (May-Jun 2021), p. 23-30
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE
- Rights
- Open Access
- Subject
- 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 0906 Electrical and Electronic Engineering; Teamwork; Intelligent systems; Anomaly detection; Computational complexity; Measurement; Collaboration
- Full Text
- Reviewed
- Funder
- This work was supported in part by the National Natural Science Foundation of China under Grant 61872054 and in part by the Fundamental Research Funds for the Central Universities under Grant DUT19LAB23.
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