fdata-02-00039_Is Performance of Scholars Correlated to Their Research Collaboration Patterns?.xml

This study aims to validate whether the research performance of scholars correlates with how the scholars work together. Although the most straightforward approaches are centrality measurements or community detection, scholars mostly participate in multiple research groups and have different roles in each group. Thus, we concentrate on the subgraphs of co-authorship networks rooted in each scholar that cover (i) overlapping of the research groups on the scholar and (ii) roles of the scholar in the groups. This study calls the subgraphs “collaboration patterns” and applies subgraph embedding methods to discover and represent the collaboration patterns. Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles. Then, we have examined whether scholars in each cluster have similar research performance, using the quantitative indicators. The coherence of the indicators cannot be solid proofs for validating the correlation between collaboration and performance. Nevertheless, the examination for clusters has exhibited that the collaboration patterns can reflect research styles of scholars. This information will enable us to predict the research performance more accurately since the research styles are more consistent and sustainable features of scholars than a few high-impact publications.

本研究旨在验证学者的科研绩效与其协作方式之间是否存在关联。尽管最直观的分析方法为中心性测度或社区发现（community detection），但学者通常会参与多个研究团队，并在不同团队中承担各异的角色。因此，本研究聚焦于以每位学者为根节点的合著网络子图，此类子图覆盖两方面内容：(i) 该学者所参与的研究团队的重叠情况，以及(ii) 该学者在各团队中所扮演的角色。本研究将此类子图定义为“协作模式”，并采用子图嵌入（subgraph embedding）方法来挖掘并表征这些协作模式。基于协作模式的嵌入结果，我们依据学者的协作风格对其进行聚类。随后，我们通过量化指标检验各聚类内的学者是否具备相似的科研绩效。尽管指标间的一致性无法作为验证协作与绩效间关联的确凿证据，但针对聚类的分析结果表明，协作模式能够反映学者的科研风格。相较于少量高影响力论文，科研风格是学者更为稳定且持久的特征，因此该信息可帮助我们更精准地预测学者的科研绩效。