Science· 2016Q1
Higher-order organization of complex networks
- 1,287citations
- Q1SCImago
- 2016year
Short summary
A new generalized framework reveals higher-order organizational structures in complex networks by clustering based on subgraph connectivity, demonstrated on neuronal and transportation networks.
AI-generated from the title and abstract; the full text is not read.
Key points
- Developed a generalized framework for clustering networks based on higher-order connectivity patterns (subgraphs).
- Framework provides mathematical guarantees on cluster optimality.
- Scales efficiently to networks with billions of edges.
- Revealed higher-order organization in neuronal and transportation networks.
AI-generated from the title and abstract; the full text is not read.
Abstract
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.
The authors' abstract, as published at the source. Science, 2016 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy