Evolving Clustered Random Networks

Abstract

We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus serve as generic models for studying the impacts of degree distributions and clustering on dynamical processes as well as null models for detecting other structural properties in empirical networks.

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• Random Graph Modeling: A survey of the concepts
• Higher-order structure and epidemic dynamics in clustered networks
• Modelling Epidemics on Networks
• Generating Simple Directed Social Network Graphs for Information Spreading
• Rich-Club Ordering and the Dyadic Effect: Two Interrelated Phenomena
• Generalized Rich-Club Ordering in Networks
• Multiscale Network Generation
• Motifs in Triadic Random Graphs based on Steiner Triple Systems
• Compact pairwise models for epidemics with multiple infectious stages on degree heterogeneous and clustered networks
• Generalized preferential attachment: tunable power-law degree distribution and clustering coefficient
• Effect of clustering on Turing instability in complex networks
• Designing Networks: A Mixed-Integer Linear Optimization Approach
• Algorithms for Generating Large-scale Clustered Random Graphs
• On the Role of Triadic Substructures in Complex Networks
• 2.5K-Graphs: from Sampling to Generation
• Fast generation of simple directed social network graphs with reciprocal edges and high clustering
• Assortativity in Generalized Preferential Attachment Models