Hyperbolic Graph Generator
arXiv:1503.05180 · doi:10.1016/j.cpc.2015.05.028
Abstract
Networks representing many complex systems in nature and society share some common structural properties like heterogeneous degree distributions and strong clustering. Recent research on network geometry has shown that those real networks can be adequately modeled as random geometric graphs in hyperbolic spaces. In this paper, we present a computer program to generate such graphs. Besides real-world-like networks, the program can generate random graphs from other well-known graph ensembles, such as the soft configuration model, random geometric graphs on a circle, or Erdős-Rényi random graphs. The simulations show a good match between the expected values of different network structural properties and the corresponding empirical values measured in generated graphs, confirming the accurate behavior of the program.
7 pages, 2 figures
References in corpus (2)
Cited by in corpus (13)
- Scale-free Networks Well Done
- Comparative analysis of two discretizations of Ricci curvature for complex networks
- PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs
- Link prediction with hyperbolic geometry
- Sparse Maximum-Entropy Random Graphs with a Given Power-Law Degree Distribution
- Memory selection and information switching in oscillator networks with higher-order interactions
- Persistent homology of unweighted complex networks via discrete Morse theory
- Finding shortest and nearly shortest path nodes in large substantially incomplete networks
- Random hyperbolic graphs in dimensions
- Weighted hypersoft configuration model
- Querying Probabilistic Neighborhoods in Spatial Data Sets Efficiently
- Hamiltonian Dynamics of Preferential Attachment
- Graph Distance from the Topological View of Non-backtracking Cycles