Research teams have been well recognized as the norm in modern scientific discovery. Rather than a loose collection of researchers, a well-performing research team is composed of a number of researchers, each of them playing a particular role (i.e., principal investigator, sub-investigator or research staff) for a short- or long-term effort. Role analysis for research teams would help gain insights into the dynamics of teams and the behavior of their members. In this paper, we address the problem of research role identification for large research institutes in which similar yet separated teams coexist. In particular, we represent a research team as teamwork networks and generate the feature representation of each member using a number of network metrics. Afterward, we propose RankMVM, short for Ranking Multi-View Machines, to learn the role identification model. Compared with traditional predictive models, RankMVM is advantageous in exploring high-order feature interactions in an efficient way, as well as handling the specific challenges of the research role identification task, including partially ordered learning targets and sparse feature interactions. In the experiments, we assess the performance on a real-world research team dataset. Extensive experimental evaluations verify the advantages of our proposed research role identification approach.

研究团队已被公认为现代科学发现中的规范。一个表现良好的研究团队不是由零散的研究人员组成，而是由许多研究人员组成，每个研究人员在短期或长期中都扮演着特定的角色（即首席研究人员，副研究人员或研究人员）努力。研究团队的角色分析将有助于深入了解团队的动态及其成员的行为。在本文中，我们解决了大型研究机构的研究角色识别问题，在这些研究机构中，类似但又相互独立的团队共存。特别是，我们将研究团队表示为团队合作网络，并使用许多网络指标来生成每个成员的特征表示。之后，我们提出RankMVM，这是对多视图计算机进行排名的缩写，学习角色识别模型。与传统的预测模型相比，RankMVM在以有效方式探索高阶特征交互以及处理研究角色识别任务的特定挑战（包括部分有序学习目标和稀疏特征交互）方面具有优势。在实验中，我们评估真实研究团队数据集的性能。大量的实验评估证明了我们提出的研究角色识别方法的优势。在实验中，我们评估真实研究团队数据集的性能。大量的实验评估证明了我们提出的研究角色识别方法的优势。在实验中，我们评估真实研究团队数据集的性能。大量的实验评估证明了我们提出的研究角色识别方法的优势。