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A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy
Xuegong Chen1, Jie Zhou1, Zhifang Liao1
1School of Computer Science and Engineering, Central South University, Changsha 470075, China.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
This study introduces TsallisRank, a new method using Tsallis entropy to better evaluate node propagation capabilities in social networks. This approach improves upon simple degree analysis for applications like network monitoring and rumor control.
Area of Science:
- Network Science
- Information Theory
- Computational Social Science
Background:
- Evaluating node propagation capabilities is crucial for social network analysis.
- Current methods relying on node degree are limited in effectiveness.
- Applications include network monitoring and rumor control.
Purpose of the Study:
- To propose a novel method for detecting node propagation ability in networks.
- To improve upon existing degree-based analysis methods.
- To introduce the TsallisRank algorithm based on Tsallis entropy.
Main Methods:
- Utilizing Tsallis entropy to assess node propagation.
- Considering the relationship between a node's Tsallis entropy and its neighbors.
- Employing the Susceptible-Infectious-Recovered (SIR) model for validation.
Main Results:
- The TsallisRank algorithm effectively evaluates node propagation ability.
- The method demonstrates accuracy in real-world network scenarios.
- Experimental results validate the proposed approach.
Conclusions:
- TsallisRank offers a more comprehensive approach to evaluating node propagation.
- The method provides enhanced accuracy compared to traditional degree-based analyses.
- This research contributes to more effective network monitoring and rumor control strategies.
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