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Research on influence spread of scientific research team based on scientific factor quantification of big data

机译：基于大数据科学因子量化的科研团队影响力传播研究

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摘要

With the development of science and technology, the interactions among scientific research teams become more and more frequent, and their relationship and behavior become more and more complex. Many researches mainly adopt complex network to analyze, but these researches only consider some aspects of scientific research factors, so lack of comprehensive consideration. From the aspect of ability, resource, activity, and familiarity, scientific research factors are quantified based on multi-source data of scientific and technological big data, and some factors of text information are similarly quantified. Based on paper citation and project cooperation, a complex network which takes scientific research team as node is constructed and is weighted by quantification of scientific research factor. The experiment of influence spread is carried out by the comparison of unweighted network and weighted network, the comparison of single node and multiple nodes, and the comparison of influence spread and other index. The results show that the scientific research factor is closely related to the influence spread; the proposed scientific research factor quantification improves the analysis of scientific research team relationship. The relationship between influence spread and the number of related communities is greater than the number of adjacent nodes. In addition, the influence spread can effectively reflect the importance of scientific research team.
机译：随着科学技术的发展，科研团队之间的互动越来越频繁，他们之间的关系和行为也越来越复杂。许多研究主要采用复杂的网络进行分析，但这些研究仅考虑了科研因素的某些方面，因此缺乏综合考虑。从能力，资源，活动和熟悉度方面，基于科技大数据的多源数据对科研因素进行量化，对文本信息的某些因素进行类似的量化。在论文引文和项目合作的基础上，构建了一个以科研团队为节点的复杂网络，并通过对科研因子的量化进行加权。通过对未加权网络与加权网络的比较，单节点与多节点的比较以及影响扩散与其他指标的比较，进行影响扩散的实验。结果表明，科研因素与影响力的传播密切相关。拟议的科研因素量化改进了科研团队关系的分析。影响扩散与相关社区数量之间的关系大于相邻节点的数量。此外，影响力的传播可以有效地反映科研团队的重要性。

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