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Complete trails of coauthorship network evolution.

Deokjae Lee¹, K-I Goh, B Kahng

¹Department of Physics and Astronomy, Seoul National University, Seoul 151-747, Korea.

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|September 28, 2010

Summary

This summary is machine-generated.

Scientific fields evolve through social networks. Coauthorship data reveals three stages: nucleation, aggregation into a giant component, and network entanglement. This network structure is reproducible via modeling.

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Molecular Evolution of the Tre Recombinase

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Molecular Evolution of the Tre Recombinase

Molecular Evolution of the Tre Recombinase

Published on: May 29, 2008

Area of Science:

Sociology of Science
Network Science
Theoretical Physics

Background:

The evolution of scientific fields depends on both intrinsic value and scientist collaboration.
Social networks, specifically coauthorship relations, offer a quantifiable measure of scientific coordination.
Digital records enable tracking of these networks over time.

Purpose of the Study:

To analyze the complete evolutionary trajectory of coauthorship networks in theoretical physics.
To identify and characterize the key processes governing network development, particularly during early stages.

Main Methods:

Utilizing digital coauthorship data from theoretical physics research.
Applying network analysis techniques to track network evolution from inception.
Comparing observed patterns with a network model for reproducibility.

Main Results:

Coauthorship networks exhibit three distinct evolutionary phases: nucleation, treelike giant component formation via aggregation, and large-scale loop entanglement.
The giant component, representing the field's core, demonstrates dynamic stability despite link degradation.
Observed network evolution patterns were successfully replicated using a network model.

Conclusions:

The study quantifies the social network dynamics underlying scientific field evolution.
Coauthorship network analysis provides insights into the structural development and robustness of research fields.
A network model can effectively reproduce the observed evolutionary patterns, validating the proposed mechanisms.