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Forming Dream Teams: A Chemistry-Oriented Approach in Social Networks

机译:形成梦想团队:社交网络中的化学方面的方法

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Scientific collaboration networks are social networks in which vertices represent scientists and edges typically represent co-authorship. Such networks not only permit research into understanding the characteristics of scientific collaboration, but can also provide a basis for building collaborative research platforms to support research groups with functionality such as, information sharing, data repositories, and communication. Collaboration networks are highly clustered, mapping closely to the real world relationships of individual researchers. However, just as big data constitute a well recognised disruptive change to the way basic research is carried out in many fields, there is an equivalent and largely unexplored change in the collaborative relationships between researchers - which are becoming not only larger in scale, but also more distributed and interdisciplinary. One element in this, which we suggest will play a pivotal role in the future, is the formation of teams for big data projects. This paper presents an innovative algorithm for expert team formation called Chemistry Oriented Team Formation (ChemoTF) based on two new metrics; Chemistry Level and Expertise Level. Chemistry Level measures scale of communication required by the task, while Expertise Level measures the overall expertise among potential teams filtered by Chemistry Level. This approach is tested using a large scholarly corpus containing 472,365 individual authors. The ChemoTF algorithm is able to build teams for median average 90 percent of the expected cost, achieving a 99 percent fit while remaining tractable for teams up to 16 individuals - resulting in the formation of more communicative and cost effective teams with higher expertise level.
机译:科学协作网络是社交网络,其中顶点代表科学家和边缘通常代表共同作者。这些网络不仅允许研究理解科学协作的特征,而且还可以为构建协作研究平台提供基础,以支持具有功能,信息共享,数据存储库和通信等功能的研究组。协作网络具有高度集群,密切绘制个人研究人员的真实世界关系。然而,正如大数据构成一个公认的破坏性变化,基本研究在许多领域进行了基础研究的方式,研究人员之间的协作关系存在相当于和很大程度上的变化 - 这在规模中不仅变得更大,而且也是如此更多分布式和跨学科。我们建议的一个元素将来会在未来发挥关键作用,是大数据项目的团队的形成。本文提出了一种基于两项新指标的专家团队组建专家团队组建的创新算法;化学等级和专业级别。化学水平衡量任务所需的沟通规模,而专业知识水平衡量化学层面过滤过滤的潜在团队的整体专业知识。使用包含472,365个单独作者的大学学术语料来测试这种方法。 Chemotf算法能够为中位数平均90%的预期成本构建团队,实现99%的拟合,同时为最多16个个人的团队留下了遗憾 - 导致形成更加沟通和具有更高专业知识水平的贸易和成本效益的团队。

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