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Collaborative Team Recognition: A Core Plus Extension Structure

Shuo Yu, Fayez Alqahtani, Amr Tolba, Ivan Lee, Tao Jia, Feng Xia

TL;DR

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.

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.

Collaborative Team Recognition: A Core Plus Extension Structure

TL;DR

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.

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.
Paper Structure (24 sections, 9 equations, 13 figures, 3 tables, 3 algorithms)

This paper contains 24 sections, 9 equations, 13 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Scientific Evaluation Metrics
  4. Collaborative Frequency
  5. Partnership Ability Index
  6. Collaboration Intensity Index
  7. Academic Age
  8. Academic Vitality
  9. Problem Definition
  10. Design of CORE
  11. Constructing CII Network
  12. Finding Core Teams
  13. Recognizing Collaborative Teams
  14. Complexity Analysis
  15. Experimental Results
  16. ...and 9 more sections

Figures (13)

  • Figure 1: CORE framework. With the input co-author network, CORE consists of three steps: (1) constructing CII network, (2) finding core teams, and (3) recognizing collaborative teams. Different labels are then assigned to nodes in different collaborative teams. Subgraphs with the same labels and edges between them are considered recognized teams.
  • Figure 2: "Core + extension" team structure.
  • Figure 3: Average collaborators per paper and the collaborative publication ratio in MAG-CS from year 2006 to 2017.
  • Figure 4: Distribution of publications over time.
  • Figure 5: Team-scale distributions identified by CORE and other baseline algorithms. In (a) to (e), the numbers represent the percentages of recognized team scales for each method, and darker colors refer to higher proportions. In (f), the general distributions of recognized teams are shown.
  • ...and 8 more figures