Generating large scale‐free networks with the Chung–Lu random graph model

DOI10.1002/net.22012zbMath1528.05059arXiv1910.11341OpenAlexW3112055712MaRDI QIDQ6087154

Unnamed Author, Dario Fasino, Francesco Tudisco

Publication date: 11 December 2023

Published in: Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.11341

zbMATH Keywords

random graphsgenerative modelsgiant componentscale-free networksChung-Lu model

Mathematics Subject Classification ID

Small world graphs, complex networks (graph-theoretic aspects) (05C82) Random graphs (graph-theoretic aspects) (05C80)

Cites Work

• Unnamed Item
• Unnamed Item
• The Bass diffusion model on networks with correlations and inhomogeneous advertising
• The expected adjacency and modularity matrices in the degree corrected stochastic block model
• Connected components in random graphs with given expected degree sequences
• A modularity based spectral method for simultaneous community and anti-community detection
• A Random Graph Model for Power Law Graphs
• An Algebraic Analysis of the Graph Modularity
• A Scalable Generative Graph Model with Community Structure
• Efficient Generation of Networks with Given Expected Degrees
• Statistical mechanics of complex networks
• Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
• The Volume of the Giant Component of a Random Graph with Given Expected Degrees
• Power-Law Distributions in Empirical Data
• The Structure and Function of Complex Networks
• The Spectra of Random Graphs with Given Expected Degrees
• The Average Distance in a Random Graph with Given Expected Degrees
• Coin-Flipping, Ball-Dropping, and Grass-Hopping for Generating Random Graphs from Matrices of Edge Probabilities
• The diameter of sparse random graphs

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