期刊
SCIENCE
卷 353, 期 6295, 页码 163-166出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.aad9029
关键词
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资金
- Stanford Graduate Fellowship
- NSF [CCF-1149756, IIS-1422918, IIS-1149837, CNS-1010921]
- trans-NIH initiative Big Data to Knowledge (BD2K)
- Defense Advanced Research Projects Agency [XDATA]
- Defense Advanced Research Projects Agency [Simplifying Complexity in Scientific Discovery (SIMPLEX)]
- Boeing
- Lightspeed
- Volkswagen
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1149756] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1010921] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1422918] Funding Source: National Science Foundation
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.
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