Random Graph Modeling: A survey of the concepts

Abstract

Random graph (RG) models play a central role in the complex networks analysis. They help to understand, control, and predict phenomena occurring, for instance, in social networks, biological networks, the Internet, etc. Despite a large number of RG models presented in the literature, there are few concepts underlying them. Instead of trying to classify a wide variety of very dispersed models, we capture and describe concepts they exploit considering preferential attachment, copying principle, hyperbolic geometry, recursively defined structure, edge switching, Monte Carlo sampling, etc. We analyze RG models, extract their basic principles, and build a taxonomy of concepts they are based on. We also discuss how these concepts are combined in RG models and how they work in typical applications like benchmarks, null models, and data anonymization.

References in corpus (8)

• Modularity and community structure in networks
• Benchmark graphs for testing community detection algorithms
• The structure and dynamics of multilayer networks
• Hyperbolic Geometry of Complex Networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Fractal and Transfractal Recursive Scale-Free Nets
• Multifractal Network Generator
• Detection of node group membership in networks with group overlap

Cited by in corpus (2)

• Random Graphs with Prescribed $K$-Core Sequences: A New Null Model for Network Analysis
• Node Copying: A Random Graph Model for Effective Graph Sampling