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Related Experiment Video

Updated: Apr 10, 2026

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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Temporal motifs reveal collaboration patterns in online task-oriented networks.

Qi Xuan^1,2, Huiting Fang¹, Chenbo Fu^1,2

¹Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 13, 2015

Summary

Temporal motifs in task-oriented social networks (TOSNs) reveal how communication and collaboration impact software development. These findings offer a more precise understanding of programmer productivity and team dynamics.

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Area of Science:

Social Network Analysis
Computer Science
Software Engineering

Background:

Real-world networks exhibit complex interactions and temporal dynamics.
Task-Oriented Social Networks (TOSNs) involve teams collaborating on artifacts, balancing work and communication.
Existing models often overlook temporal changes and node/link heterogeneity in TOSNs.

Purpose of the Study:

To develop and apply a methodology for identifying temporal motifs in two-person, artifact-centric TOSNs.
To analyze the impact of temporal motifs on software development outcomes.
To compare the predictive power of temporal motif-based models against aggregated models.

Main Methods:

Developed a methodology for finding temporal motifs in TOSNs.
Applied the methodology to publicly available data from 31 Open Source Software projects.
Utilized temporal motifs to model relationships between developer centrality, team cohesion, and programmer productivity.

Main Results:

Temporal motifs were found to be enriched in the observed TOSN data.
A distinct dependency between collaboration and communication in code writing was revealed.
Temporal motif-based models demonstrated a more precise relationship between network properties and programmer productivity.

Conclusions:

Temporal motifs provide a more nuanced understanding of TOSN behavior and outcomes.
The interplay of communication and collaboration, captured by temporal motifs, is crucial for software development success.
Temporal motif analysis offers superior insights into programmer productivity compared to aggregated network models.