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Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold.

Ronghua Shang, Weitong Zhang, Licheng Jiao

IEEE Transactions on Cybernetics

|May 27, 2020

Summary

This summary is machine-generated.

This study introduces a novel network immunization model that uses community structure and node similarity to prevent the spread of harmful information. The proposed method demonstrates superior effectiveness in network immunization compared to existing approaches.

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Area of Science:

Network Science
Information Security
Computational Social Science

Background:

The rapid growth of big data and complex networks presents challenges in controlling the spread of harmful information, such as computer viruses.
Understanding network propagation dynamics and developing effective node immunization strategies are crucial for mitigating such threats.

Purpose of the Study:

To propose a novel dynamic node immune model, termed NICT (Network Immune based on Community structure and Threshold), for restraining information propagation.
To enhance network immunization strategies by incorporating community structure and node similarity.
To introduce a new evaluation index for assessing the effectiveness of network immunization.

Main Methods:

A network model is established where infected nodes are treated as new network entities, with adaptable edge creation methods.
Propagation probability is determined using community structure information and a node similarity function.
An improved immune gain is calculated based on propagation probability and node similarity, with immunization triggered by an immune threshold parameter.
A new evaluation index considers both immune and infected nodes over time.

Main Results:

The proposed NICT model effectively prevents invalid or insufficient immunization by calculating immune gain at each time step.
Experimental results on eight real-world networks show that the NICT model achieves better network immunization outcomes.
The proposed evaluation index provides a more effective assessment of node immunization effects.

Conclusions:

The NICT model offers a significant advancement in network immunization strategies, particularly for large and complex networks.
Incorporating community structure and node similarity enhances the precision and efficiency of immunization efforts.
The developed evaluation index offers a more comprehensive measure of immunization success.