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Collaborative Team Recognition: A Core Plus Extension Structure

arXiv 2406.06617 ↗First posted 7 Jun 2024cs.SI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 59 references that are in the map. First posted on arXiv 7 Jun 2024.

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References · 13

  1. 2019Representation Learning for Attributed Multiplex Heterogeneous Networkcited by 91
  2. 2017An Attention-based Collaboration Framework for Multi-View Network Representation Learningcited by 31
  3. 2019How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?cited by 30
  4. 2018AceKG: A Large-scale Knowledge Graph for Academic Data Miningcited by 28
  5. 2017A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovationscited by 13
  6. 2020Multivariate Relations Aggregation Learning in Social Networkscited by 10
  7. 2017Quantifying patterns of research interest evolutioncited by 8
  8. 2020Data-driven Computational Social Science: A Surveycited by 8
  9. 2014Principles of scientific research team formation and evolutioncited by 7
  10. 2020Big Networks: A Surveycited by 6
  11. 2022CHIEF: Clustering with Higher-order Motifs in Big Networkscited by 3
  12. 2021Turing Award elites revisited: patterns of productivity, collaboration, authorship and impactcited by 1
1 more
  1. 2022Familiarity-based Collaborative Team Recognition in Academic Social Networkscited by 1

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