Scientific collaboration is a significant behavior in knowledge creation and
idea exchange. To tackle large and complex research questions, a trend of team
formation has been observed in recent decades. In this study, we focus on
recognizing collaborative teams and exploring inner patterns using scholarly
big graph data. We propose a collaborative team recognition (CORE) model with a
"core + extension" team structure to recognize collaborative teams in large
academic networks. In CORE, we combine an effective evaluation index called the
collaboration intensity index with a series of structural features to recognize
collaborative teams in which members are in close collaboration relationships.
Then, CORE is used to guide the core team members to their extension members.
CORE can also serve as the foundation for team-based research. The simulation
results indicate that CORE reveals inner patterns of scientific collaboration:
senior scholars have broad collaborative relationships and fixed collaboration
patterns, which are the underlying mechanisms of team assembly. The
experimental results demonstrate that CORE is promising compared with
state-of-the-art methods.

科学合作是知识创造和思想交流中的重要行为。本研究聚焦于识别协作团队和探索其内部模式，使用学术大图数据。提出一种基于 “核心 + 扩展” 团队结构的协作团队识别（CORE）模型，以在大型学术网络中识别协作团队。CORE 将协作强度指标与一系列结构特征相结合，以识别成员之间具有紧密合作关系的协作团队，并指导核心团队成员寻找扩展成员。CORE 还可作为基于团队的研究的基础。模拟结果显示 CORE 揭示了科学合作的内部模式：资深学者拥有广泛的合作关系和固定的合作模式，是团队组装的基本机制。实验结果表明，与现有方法相比，CORE 具有很大的潜力。