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How zealots affect the energy cost for controlling complex social networks
1Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
Chaos (Woodbury, N.Y.)
|July 1, 2022
Summary
Introducing zealots (stubborn agents) into complex network models significantly impacts control energy. Their presence can increase or decrease energy costs depending on network dynamics and agent beliefs.
Area of Science:
- Network Science
- Sociophysics
- Control Theory
Background:
- Controllability of complex networks is crucial for understanding social systems.
- Existing models often assume neutral information flow among nodes.
- This assumption is limited as individuals hold personal beliefs and influence others.
Purpose of the Study:
- To investigate the impact of 'zealots' (agents with fixed beliefs) on the control energy of complex networks.
- To compare control energy in networks with and without zealots.
- To analyze how zealot characteristics and network parameters influence control energy.
Main Methods:
- Introduced stubborn agents ('zealots') into network models.
- Computed and compared control energy with and without zealots.
- Analyzed the quadratic relationship between zealot beliefs and energy cost.
- Examined the influence of network dynamics, conformity, and node configurations.
Main Results:
- The presence of zealots alters control energy quadratically with respect to their beliefs.
- The effect of zealots on energy cost is parameter-dependent, involving zealot beliefs, network dynamics, and node configurations.
- Contrarian zealots can reduce energy cost in linear dynamics without conformity but increase it with conformity.
- Network control energy is sensitive to parameter changes when modeling social systems.
Conclusions:
- Models of networked social systems must account for non-neutral agent behavior (zealots).
- The assumption of nodal neutrality is insufficient for accurate control energy assessment.
- System dynamics and parameter interplay are critical for understanding network controllability with non-neutral agents.
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