Collaborative Team Recognition: A Core Plus Extension Structure

TL;DR

This paper introduces the CORE model for recognizing collaborative research teams in large academic networks, revealing patterns of scientific collaboration and outperforming existing methods.

Contribution

The paper proposes a novel 'core + extension' team recognition model that effectively identifies collaborative teams and their internal structures in scholarly big graph data.

Findings

CORE reveals senior scholars' broad collaboration patterns

The model outperforms state-of-the-art methods in team recognition

It provides insights into team assembly mechanisms

Abstract

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.