Dynamic identification of important nodes in complex networks based on the KPDN-INCC method.
Jieyong Zhang1, Liang Zhao2, Peng Sun3
1Information and Navigation College, Air Force Engineering University, Xi'an, 710077, China.
Scientific Reports
|March 9, 2024
Summary
Identifying crucial nodes in dynamic networks is challenging. This study proposes a new method integrating local and global network properties to accurately pinpoint important nodes, aiding in network disintegration and improving upon existing techniques.
Area of Science:
- Complex networks analysis
- Network science
- Systems engineering
Background:
- Cascading failures pose significant risks in complex networks.
- Existing node importance evaluation methods are often inadequate for dynamic network scenarios.
- Distinguishing between static and dynamic network node identification is crucial.
Purpose of the Study:
- To develop a robust node importance evaluation method for dynamic complex networks.
- To address limitations of static network methods when applied to dynamic systems.
- To enhance the accuracy of identifying critical nodes for network disintegration.
Main Methods:
- Integration of local and global correlation properties for node evaluation.
- Improved k-shell method with fusion degree for enhanced global node ranking.
- Incorporation of improved Solton and structure hole factors (via INCC) for local node relationship identification.
Main Results:
- The proposed KPDN-INCC method accurately identifies important nodes in dynamic networks.
- This method complements existing KPDN techniques, offering improved performance.
- Effectiveness demonstrated in small-world networks with low randomness.
Conclusions:
- The KPDN-INCC method provides a superior approach for node importance evaluation in dynamic complex networks.
- Accurate identification of critical nodes facilitates efficient network disintegration.
- The findings contribute to a better understanding of network resilience and vulnerability.
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