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Related Experiment Video

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Research on the Influence of Information Iterative Propagation on Complex Network Structure.

Yinuo Qian¹, Fuzhong Nian¹, Zheming Wang¹

¹School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China.

|July 27, 2024

Summary

This study visualizes how information propagation dynamics alter network structures. It introduces node participation to quantify user influence on information spread and analyzes micro (chain edges) and macro (community division) structural changes.

Keywords:

complex network information iterative propagation link prediction propagation weighted network

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Area of Science:

Network Science
Information Dynamics
Complex Systems

Background:

Network structures evolve due to dynamic propagation processes.
Iterative information propagation influences connection strengths and network evolution over time.
Existing research often focuses on temporal network construction, overlooking propagation dynamics' impact on structure.

Purpose of the Study:

To visualize and quantify how propagation dynamics influence complex network structure changes.
To analyze both micro-level (chain edges) and macro-level (community division) structural alterations.
To propose a metric for quantifying user influence on information propagation.

Main Methods:

Constructing weighted networks based on iterative information propagation analysis.
Simulating information propagation in diverse network types.
Proposing and applying 'node participation' to quantify user influence.

Main Results:

Demonstrated that iterative propagation leads to micro-level changes like chain edge formation.
Showcased macro-level structural changes through community division analysis.
Successfully quantified the influence of different users on information propagation using node participation.

Conclusions:

Information propagation dynamics are key drivers of complex network structural evolution.
Node participation effectively quantifies individual user impact on network-wide information spread.
Analyzing chain edges and community structures provides a comprehensive understanding of propagation's influence.