2016/12/26 by Austin R. Benson, David F. Gleich, Jure Leskovec · 3 citations
Computer Science · Physics and Astronomy · #cs.SI #cs.DM #physics.soc-ph
paper · pdf · doi:10.1126/science.aad9029
published as Science, 353.6295 (2016): 163--166
arxiv created 2016/12/26 · arxiv updated 2018/01/08
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 based on 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.