# Collaborative Team Recognition: A Core Plus Extension Structure

arXiv: https://arxiv.org/abs/2406.06617 · HTML: https://mlsanity.com/paper/2406.06617/

## 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.

## Builds on (most cited first, 13 of 13)

- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [An Attention-based Collaboration Framework for Multi-View Network Representation Learning](https://mlsanity.com/paper/1709.06636/) (2017)
- [How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?](https://mlsanity.com/paper/1909.03486/) (2019)
- [AceKG: A Large-scale Knowledge Graph for Academic Data Mining](https://mlsanity.com/paper/1807.08484/) (2018)
- [A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations](https://mlsanity.com/paper/1704.05150/) (2017)
- [Multivariate Relations Aggregation Learning in Social Networks](https://mlsanity.com/paper/2008.03654/) (2020)
- [Quantifying patterns of research interest evolution](https://mlsanity.com/paper/1709.03319/) (2017)
- [Data-driven Computational Social Science: A Survey](https://mlsanity.com/paper/2008.12372/) (2020)
- [Principles of scientific research team formation and evolution](https://mlsanity.com/paper/1403.2787/) (2014)
- [Big Networks: A Survey](https://mlsanity.com/paper/2008.03638/) (2020)
- [CHIEF: Clustering with Higher-order Motifs in Big Networks](https://mlsanity.com/paper/2204.02656/) (2022)
- [Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact](https://mlsanity.com/paper/2106.11534/) (2021)
- [Familiarity-based Collaborative Team Recognition in Academic Social Networks](https://mlsanity.com/paper/2204.02667/) (2022)

## 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.