Pinning controllability of complex networks with community structure
Qingying Miao1, Yang Tang, Jürgen Kurths
1School of Continuing Education, Shanghai Jiao Tong University, Shanghai 200030, China.
Chaos (Woodbury, N.Y.)
|October 5, 2013
Summary
Network controllability improves with more communities and weaker community structures. Evenly distributed pinning schemes are most effective, with degree descending best for few nodes and degree ascending for many.
Area of Science:
- Network Science
- Complex Systems Analysis
Background:
- Understanding network controllability is crucial for designing robust and functional complex systems.
- Community structure significantly impacts network dynamics and control.
- Various pinning strategies exist, but their effectiveness varies with network properties and control objectives.
Purpose of the Study:
- To investigate how network controllability is affected by the number and strength of community structures.
- To compare the performance of different node pinning schemes for enhancing controllability.
- To identify optimal strategies for controlling networks with modular organization.
Main Methods:
- Extensive simulations were conducted on networks with varying community structures.
- Multiple pinning schemes were evaluated, including degree descending and ascending approaches.
- The impact of the number of pinned nodes and their distribution across communities was analyzed.
Main Results:
- The degree descending pinning scheme excels when few nodes are pinned.
- The degree ascending pinning scheme becomes more effective as the number of pinned nodes increases.
- Increasing the number of communities or decreasing community structure strength enhances controllability.
- Evenly distributing pinned nodes across communities yields superior control performance.
Conclusions:
- Network controllability is significantly influenced by community structure and the chosen pinning strategy.
- Optimizing node pinning distribution and selection is key to achieving effective network control.
- Findings provide insights for designing and managing complex networks with modular architectures.
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