Generating Realistic Labelled, Weighted Random Graphs

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only struc...

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Bibliographic Details
Main Authors: Michael Charles Davis, Zhanyu Ma, Weiru Liu, Paul Miller, Ruth Hunter, Frank Kee
Format: Article
Language:English
Published: MDPI AG 2015-12-01
Series:Algorithms
Subjects:
network models
generative algorithms
random graphs
labelled graphs
weighted graphs
bayesian estimation
maximum likelihood estimation
beta distribution
mixture modeling
variational inference
Online Access:http://www.mdpi.com/1999-4893/8/4/1143

Internet

http://www.mdpi.com/1999-4893/8/4/1143

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