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Related Experiment Video

Updated: Jan 8, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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.

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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.