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Efficient algorithms for team formation with a leader in social networks

机译:与社交网络领导者进行团队组建的高效算法

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摘要

Given a project with a set of required skills, it is an important and challenging problem of find a team of experts that have not only the required skill set but also the minimal communication cost. Furthermore, in view of the benefits of greater leaders, prior work presented the team formation problem with a leader where the leader is responsible for coordinating and managing the project. To find the best leader and the corresponding team, the prior work exhaustively evaluates each candidate and the associated team, incurring substantial computational cost. In this paper, we propose two efficient algorithms, namely the BCPruning algorithm and the SSPruning algorithm, to accelerate the discovery of the best leader and the corresponding team by reducing the search space of team formation for candidates. The BCPruning algorithm aims at selecting better initial leader candidates to obtain lower communication cost, enabling effective candidate pruning. On the other hand, the SSPruning algorithm allows each leader candidate to have a lower bound on the communication cost, leading some candidates to be safely pruned without any computation. Besides, the SSPruning algorithm exploits the exchanged information among experts to aid initial candidate selection as well as team member search. For performance evaluation, we conduct experiments using a real dataset. The experimental results show that the proposed BCPruning and SSPruning algorithms are respectively 1.42-1.68 and 2.64-3.25 times faster than the prior work. Moreover, the results indicate that the proposed algorithms are more scalable than the prior work.
机译:对于一个具有所需技能的项目,找到一个不仅具有所需技能而且还具有最低沟通成本的专家团队是一个重要且具有挑战性的问题。此外,鉴于更大的领导者的利益,先前的工作向领导者提出了团队组建问题,领导者负责项目的协调和管理。为了找到最佳的领导者和相应的团队,先前的工作详尽地评估了每个候选人和相关的团队,从而产生了大量的计算成本。在本文中,我们提出了两种有效的算法,即BCPruning算法和SSPruning算法,以通过减少候选人组队的搜索空间来加快最佳领导者和相应团队的发现。 BCPruning算法旨在选择更好的初始领导者候选人以获得较低的通信成本,从而实现有效的候选人修剪。另一方面,SSPruning算法允许每个领导者候选者在通信成本上有一个下限,从而使某些候选者无需进行任何计算就可以安全地被修剪。此外,SSPruning算法利用专家之间交换的信息来帮助初始候选人选择以及团队成员搜索。为了进行性能评估,我们使用真实的数据集进行实验。实验结果表明,所提出的BCPruning和SSPruning算法分别比先前的工作快1.42-1.68倍和2.64-3.25倍。此外,结果表明,所提出的算法比现有技术更具可扩展性。

著录项

  • 来源
    《Journal of supercomputing》 |2013年第2期|721-737|共17页
  • 作者单位

    Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC;

    Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC;

    Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Social network; Social intelligence; Team formation;

    机译:社交网络;社会智慧;组队;

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