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Assessment of Motor Balance and Coordination in Mice using the Balance Beam

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Published on: March 10, 2011

Balance in signed networks.

Alec Kirkley¹, George T Cantwell¹, M E J Newman^1,2

¹Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Physical Review. E

|February 21, 2019

Summary

This study introduces new measures for structural balance in signed networks, finding real-world networks exhibit significant balance. These measures also improve the prediction of unknown network connections.

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Area of Science:

Social network analysis
Graph theory
Computational social science

Background:

Signed networks contain positive (e.g., friendship) and negative (e.g., conflict) edges.
Structural balance theory posits that certain edge configurations are favored in signed networks.
Existing measures of balance may not fully capture network properties.

Purpose of the Study:

To propose novel measures of structural balance in signed networks.
To evaluate these measures against existing methods and a null model.
To assess the predictive power of balance for unknown network signs.

Main Methods:

Development of two new balance measures based on weak and strong balance concepts.
Comparison of proposed measures with prior methods on various tasks.
Assessment against a null model to determine statistical significance of balance.
Cross-validation testing for predicting unknown edge signs.

Main Results:

Real-world signed networks demonstrate significant structural balance across all proposed measures.
The new balance measures outperform existing methods in several tasks.
Predicting unknown edge signs using balance maximization yields results substantially better than chance.

Conclusions:

The proposed balance measures offer a robust way to quantify structural balance in signed networks.
Empirical evidence supports the prevalence of structural balance in real-world signed networks.
Balance maximization is a viable strategy for inferring missing information in signed networks.