Mapping flows on hypergraphs
arXiv:2101.00656 · doi:10.1038/s42005-021-00634-z
Abstract
Hypergraphs offer an explicit formalism to describe multibody interactions in complex systems. To connect dynamics and function in systems with these higher-order interactions, network scientists have generalised random-walk models to hypergraphs and studied the multibody effects on flow-based centrality measures. But mapping the large-scale structure of those flows requires effective community detection methods. We derive unipartite, bipartite, and multilayer network representations of hypergraph flows and explore how they and the underlying random-walk model change the number, size, depth, and overlap of identified multilevel communities. These results help researchers choose the appropriate modelling approach when mapping flows on hypergraphs.
References in corpus (6)
- Fast unfolding of communities in large networks
- Maps of random walks on complex networks reveal community structure
- Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems
- Random walks on hypergraphs
- Mapping higher-order network flows in memory and multilayer networks with Infomap
- Inhomogeneous Hypergraph Clustering with Applications
Cited by in corpus (17)
- Dynamics on higher-order networks: A review
- Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes
- Higher-order motif analysis in hypergraphs
- Inference of hyperedges and overlapping communities in hypergraphs
- Community Detection in Large Hypergraphs
- Hyperlink communities in higher-order networks
- Structure and inference in hypergraphs with node attributes
- A framework to generate hypergraphs with community structure
- Collective dynamics on higher-order networks
- Multiplex measures for higher-order networks
- Compressing network populations with modal networks reveals structural diversity
- Measuring dynamical systems on directed hyper-graphs
- Flow-based Community Detection in Hypergraphs
- Finding Influential Cores via Normalized Ricci Flows in Directed and Undirected Hypergraphs with Applications
- Community detection in hypergraphs through hyperedge percolation
- Single-trajectory map equation
- Sampling nodes and hyperedges via random walks on large hypergraphs