Information Propagation Analysis of Social Network Using the Universality of Random Matrix
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Summary
The results show that the worst-case speed of the information propagation changes at most 2 if the structure of a social network changes, and the universality of the normalized Laplacian matrix is clarified.
- Type
- article
- Published
- 2017-12-07
- Cited by
- 4
- References
- 13
- Access
- Open access
- OpenAlex
- https://openalex.org/W2795850273
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4604964
Keywords
Laplacian matrix, Universality (dynamical systems), Spectral graph theory, Mathematics, Eigenvalues and eigenvectors
References
- Spectral Graph Theory
- RANDOM-MATRIX THEORIES IN QUANTUM PHYSICS : COMMON CONCEPTS
- On the distributions of Laplacian eigenvalues versus node degrees in complex networks
- The Spectra of Random Graphs with Given Expected Degrees
- Efficiency of scale-free networks: error and attack tolerance
- Random matrix analysis of network Laplacians
- Spectra of "real-world" graphs: beyond the semicircle law.
- An analysis of social network-based Sybil defenses
- Random graphs
- On the Distribution of the Roots of Certain Symmetric Matrices
- Fluid-Based Analysis for Understanding TCP Performance on Scale-Free Structure
- An analysis of social network-based Sybil defenses
- Emergence of Scaling in Random Networks
- Random Walks on Graphs: A Survey
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