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Fast Methods for Finding Multiple Effective Influencers in Real Networks
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Summary
This study introduces scalable methods to identify key network nodes for rapid consensus spread. These techniques efficiently find nodes that minimize the expected time for information to reach all network parts.
Area of Science:
- Network Science
- Graph Theory
- Algorithm Design
Background:
- Consensus reaching is crucial in networked systems.
- Identifying optimal nodes for fast information spread is computationally challenging.
- Existing methods struggle with scalability for large networks.
Purpose of the Study:
- To develop scalable methods for finding optimal node sets to accelerate consensus.
- To minimize the sum of first hitting times from all nodes to a selected set.
- To address the computational complexity of this problem in large graphs.
Main Methods:
- Development of scalable first hitting time algorithms.
- Leveraging characteristics of real-world networks.
- Application of Monte Carlo methods for approximation.
Main Results:
- Proposed methods provide near-optimal solutions for node selection.
- Algorithms demonstrate scalability on complex network structures.
- Efficiently identifies node collections for fastest consensus spread.
Conclusions:
- Scalable first hitting time methods offer an effective solution for optimizing consensus spread.
- Monte Carlo-based approximation algorithms are suitable for real-world network analysis.
- The findings have implications for network design and information dissemination strategies.
Keywords:
Monte Carlo methodsapproximation algorithmshitting timeinformation dynamicsnetworksrandom walksMore Related Videos
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