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Participant-invariant, evolving patterns of influence in dynamic networks
Shaojie Min1, Jiaxing Shang2, Ji Liu2
1Fudan University, Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Shanghai 200433, China.
Physical Review. E
|December 23, 2025
Summary
Dynamic network influence patterns are surprisingly consistent, often explained by just one core pattern. This finding simplifies analyzing complex systems and reveals heterogeneous node participation.
Area of Science:
- Complex Systems Science
- Network Science
- Data Science
Background:
- Understanding influence evolution in dynamic networks is key to complex systems.
- Temporal influence patterns are under-explored due to perceived analytical complexity.
- Real-world networks present challenges in analyzing numerous evolving influence processes.
Purpose of the Study:
- To uncover participant-invariant characteristics in dynamic network influence.
- To demonstrate that a small number of influence patterns can represent network behavior.
- To provide a framework for understanding influence evolution in complex systems.
Main Methods:
- Analysis of 50 dynamic network datasets from diverse domains.
- Identification of core influence patterns within networks.
- Quantification of node participation using associated weights.
Main Results:
- A small number of influence patterns (often one) capture overall network behavior.
- Influence patterns show similarities across networks of the same category.
- Node weight distribution follows a power law, indicating heterogeneous participation.
Conclusions:
- Dynamic network influence exhibits a participant-invariant characteristic.
- A simplified representation of dynamic networks is achievable.
- Insights into heterogeneous node participation and influence evolution are provided.
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