A framework to generate hypergraphs with community structure
arXiv:2212.08593 · doi:10.1103/PhysRevE.109.034309
Abstract
In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), analyze community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming previous limitations on the generation of synthetic hypergraphs, our work constitutes a substantial advancement in the statistical modeling of higher-order systems.
arXiv comment18 pages, 8 figures, revised version
References in corpus (48)
- Emergence of scaling in random networks
- Finding and evaluating community structure in networks
- Community detection in graphs
- Efficient Behavior of Small-World Networks
- Benchmark graphs for testing community detection algorithms
- Stochastic blockmodels and community structure in networks
- Community detection in networks: A user guide
- Networks beyond pairwise interactions: structure and dynamics
- The physics of higher-order interactions in complex systems
- Simplicial models of social contagion
- Synchronization reveals topological scales in complex networks
- The statistical mechanics of networks
- Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
- Evolutionary dynamics of higher-order interactions in social networks
- Simplicial Closure and higher-order link prediction
- Structure and inference in annotated networks
- Explosive higher-order Kuramoto dynamics on simplicial complexes
- Complex Networks as Hypergraphs
- Higher-order interactions in complex networks of phase oscillators promote abrupt synchronization switching
- Optimal network modularity for information diffusion
- Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes
- Random walks on hypergraphs
- Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian
- Generalized network structures: The configuration model and the canonical ensemble of simplicial complexes
- Community detection, link prediction, and layer interdependence in multilayer networks
- Multiorder Laplacian for synchronization in higher-order networks
- Higher-order motif analysis in hypergraphs
- Multi-body Interactions and Non-Linear Consensus Dynamics on Networked Systems
- Random graph models for dynamic networks
- Chaos in generically coupled phase oscillator networks with nonpairwise interactions
- Inference of hyperedges and overlapping communities in hypergraphs
- Hypergraph reconstruction from network data
- Simplicial contagion in temporal higher-order networks
- Unveiling the higher-order organization of multivariate time series
- Construction of and efficient sampling from the simplicial configuration model
- Evolutionary game model of group choice dilemmas on hypergraphs
- Weighted simplicial complexes and their representation power of higher-order network data and topology
- Growing scale-free simplices
- Community Detection in Large Hypergraphs
- Community detection with node attributes in multilayer networks
- Clustering in graphs and hypergraphs with categorical edge labels
- Mapping flows on hypergraphs
- Hypergraphx: a library for higher-order network analysis
- A class of models for random hypergraphs
- Reciprocity, community detection, and link prediction in dynamic networks
- Community detection in the sparse hypergraph stochastic block model
- Random Recursive Hypergraphs
- Not all simple looking degree sequence problems are easy