For a kinetic consensus-based segmentation model, the choice of evaluation metric changes the optimized model parameters, with Surface Dice being the most representative and F-beta unreliable.
Steering opinion dynamics through control of social networks
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abstract
In this paper we propose a novel control approach for opinion dynamics on evolving networks. The controls modify the strength of connections in the network, rather than influencing opinions directly, with the overall goal of steering the population towards a target opinion. This requires that the social network remains sufficiently connected, the population does not break into separate opinion clusters, and that the target opinion remains accessible. We present several approaches to addressing these challenges, considering questions of controllability, instantaneous control and optimal control. Each of these approaches provides a different view on the complex relationship between opinion and network dynamics and raises interesting questions for future research.
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Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-based Segmentation
For a kinetic consensus-based segmentation model, the choice of evaluation metric changes the optimized model parameters, with Surface Dice being the most representative and F-beta unreliable.